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ImageNet A Large Scale Hierarchical Image Database
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mammal placental carnivore canine dog working dog husky. vehicle craft watercraft sailing vessel sailboat trimaran. Figure 1 A snapshot of two root to leaf branches of ImageNet the top row is from the mammal subtree the bottom row is from the. vehicle subtree For each synset 9 randomly sampled images are presented. ESP Cat Subtree Imagenet Cat Subtree,Summary of selected subtrees. Avg synset Total 376,Subtree Synsets,size image,Mammal 1170 737 862K. percentage,Vehicle 520 610 317K,GeoForm 176 436 77K 1830. Furniture 197 797 157K,0 1 Bird 872 809 705K,MusicInstr 164 672 110K. ESP Cattle Subtree Imagenet Cattle Subtree,0 500 1000 1500 2000 2500 176.
images per synset, Figure 2 Scale of ImageNet Red curve Histogram of number. of images per synset About 20 of the synsets have very few. images Over 50 synsets have more than 500 images Table Figure 3 Comparison of the cat and cattle subtrees between. Summary of selected subtrees For complete and up to date statis ESP 25 and ImageNet Within each tree the size of a node is. tics visit http www image net org about stats proportional to the number of images it contains The number of. images for the largest node is shown for each tree Shared nodes. between an ESP tree and an ImageNet tree are colored in red. images spread over 5247 categories Fig 2 On average. over 600 images are collected for each synset Fig 2 shows. the distributions of the number of images per synset for the gory labels into a semantic hierarchy by using WordNet the. current ImageNet 1 To our knowledge this is already the density of ImageNet is unmatched by others For example. largest clean image dataset available to the vision research to our knowledge no existing vision dataset offers images of. community in terms of the total number of images number 147 dog categories Fig 3 compares the cat and cattle. of images per category as well as the number of categories 2 subtrees of ImageNet and the ESP dataset 25 We observe. that ImageNet offers much denser and larger trees, Hierarchy ImageNet organizes the different classes of. images in a densely populated semantic hierarchy The Accuracy We would like to offer a clean dataset at all. main asset of WordNet 9 lies in its semantic structure i e levels of the WordNet hierarchy Fig 4 demonstrates the. its ontology of concepts Similarly to WordNet synsets of labeling precision on a total of 80 synsets randomly sam. images in ImageNet are interlinked by several types of re pled at different tree depths An average of 99 7 preci. lations the IS A relation being the most comprehensive sion is achieved on average Achieving a high precision for. and useful Although one can map any dataset with cate all depths of the ImageNet tree is challenging because the. lower in the hierarchy a synset is the harder it is to classify. 1 About 20 of the synsets have very few images because either there. e g Siamese cat versus Burmese cat, are very few web images available e g vespertilian bat or the synset by. definition is difficult to be illustrated by images e g two year old horse. 2 It is claimed that the ESP game 25 has labeled a very large number Diversity ImageNet is constructed with the goal that ob. of images but only a subset of 60K images are publicly available jects in images should have variable appearances positions. 1 datasets are needed for the next generation of algorithms. The current ImageNet offers 20 the number of categories. and 100 the number of total images than these datasets. 1 2 3 4 5 6 7 8 9 TinyImage TinyImage 24 is a dataset of 80 million. tree depth 32 32 low resolution images collected from the Inter. net by sending all words in WordNet as queries to image. Figure 4 Percent of clean images at different tree depth levels in. search engines Each synset in the TinyImage dataset con. ImageNet A total of 80 synsets are randomly sampled at every. tree depth of the mammal and vehicle subtrees An independent tains an average of 1000 images among which 10 25 are. group of subjects verified the correctness of each of the images possibly clean images Although the TinyImage dataset has. An average of 99 7 precision is achieved for each synset had success with certain applications the high level of noise. and low resolution images make it less suitable for gen. ImageNet TinyImage LabelMe ESP LHill eral purpose algorithm development training and evalua. LabelDisam Y Y N N Y tion Compared to the TinyImage dataset ImageNet con. Clean Y N Y Y Y tains high quality synsets 99 precision and full reso. DenseHie Y Y N N N lution images with an average size of around 400 350. FullRes Y N Y Y Y,PublicAvail Y Y Y N N,Segmented N N Y N Y. ESP dataset The ESP dataset is acquired through an on. line game 25 Two players independently propose labels. Table 1 Comparison of some of the properties of ImageNet ver to one image with the goal of matching as many words as. sus other existing datasets ImageNet offers disambiguated la possible in a certain time limit Millions of images are la. bels LabelDisam clean annotations Clean a dense hierarchy beled through this game but its speeded nature also poses a. DenseHie full resolution images FullRes and is publicly avail. major drawback Rosch and Lloyd 20 have demonstrated. able PublicAvail ImageNet currently does not provide segmen. tation annotations, that humans tend to label visual objects at an easily acces.
sible semantic level termed as basic level e g bird as. opposed to more specific level sub ordinate level e g. view points poses as well as background clutter and occlu sparrow or more general level super ordinate level e g. sions In an attempt to tackle the difficult problem of quan vertebrate Labels collected from the ESP game largely. tifying image diversity we compute the average image of concentrate at the basic level of the semantic hierarchy. each synset and measure lossless JPG file size which reflects as illustrated by the color bars in Fig 6 ImageNet how. the amount of information in an image Our idea is that a ever demonstrates a much more balanced distribution of. synset containing diverse images will result in a blurrier av images across the semantic hierarchy Another critical dif. erage image the extreme being a gray image whereas a ference between ESP and ImageNet is sense disambigua. synset with little diversity will result in a more structured tion When human players input the word bank it is un. sharper average image We therefore expect to see a smaller clear whether it means a river bank or a financial insti. JPG file size of the average image of a more diverse synset tution At this large scale disambiguation becomes a non. Fig 5 compares the image diversity in four randomly sam trivial task Without it the accuracy and usefulness of the. pled synsets in Caltech101 8 3 and the mammal subtree of ESP data could be affected ImageNet on the other hand. ImageNet does not have this problem by construction See section 3 2. for more details Lastly most of the ESP dataset is not pub. 2 1 ImageNet and Related Datasets licly available Only 60K images and their labels can be. We compare ImageNet with other datasets and summa accessed 1. rize the differences in Table 1 4, LabelMe and Lotus Hill datasets LabelMe 21 and the. Small image datasets A number of well labeled small Lotus Hill dataset 27 provide 30k and 50k labeled and seg. datasets Caltech101 256 8 12 MSRC 22 PASCAL 7 mented images respectively 5 These two datasets provide. etc have served as training and evaluation benchmarks complementary resources for the vision community com. for most of today s computer vision algorithms As com pared to ImageNet Both only have around 200 categories. puter vision research advances larger and more challenging but the outlines and locations of objects are provided Im. 3 We also compare with Caltech256 12 The result indicates the diver ageNet in its current form does not provide detailed object. sity of ImageNet is comparable which is reassuring since Caltech256 was outlines see potential extensions in Sec 5 1 but the num. specifically designed to be more diverse ber of categories and the number of images per category. 4 We focus our comparisons on datasets of generic objects Special pur. pose datasets such as FERET faces 19 Labeled faces in the Wild 13 5 All statistics are from 21 27 In addition to the 50k images the. and the Mammal Benchmark by Fink and Ullman 11 are not included Lotus Hill dataset also includes 587k video frames. Lossless JPG size in byte,elephant ImageNet,Caltech101. 900 1000 1100, Figure 5 ImageNet provides diversified images a Comparison of the lossless JPG file sizes of average images for four different synsets. in ImageNet the mammal subtree and Caltech101 Average images are downsampled to 32 32 and sizes are measured in byte A more. diverse set of images results in a smaller lossless JPG file size b Example images from ImageNet and average images for each synset. indicated by a c Examples images from Caltech101 and average images For each category shown the average image is computed. using all images from Caltech101 and an equal number of randomly sampled images from ImageNet. 0 5 accuracy of image search results from the Internet is around. 10028 Imagenet 10 24 ImageNet aims to eventually offer 500 1000. clean images per synset We therefore collect a large set. percentage, 0 3 197850 of candidate images After intra synset duplicate removal. each synset has over 10K images on average, We collect candidate images from the Internet by query.
0 1 ing several image search engines For each synset the. 0 queries are the set of WordNet synonyms Search engines. 1 2 3 4 5 6 7 8 9 typically limit the number of images retrievable in the or. depth der of a few hundred to a thousand To obtain as many im. Figure 6 Comparison of the distribution of mammal labels ages as possible we expand the query set by appending the. over tree depth levels between ImageNet and ESP game The y queries with the word from parent synsets if the same word. axis indicates the percentage of the labels of the corresponding appears in the gloss of the target synset For example when. dataset ImageNet demonstrates a much more balanced distribu querying whippet according to WordNet s gloss a small. tion offering substantially more labels at deeper tree depth levels slender dog of greyhound type developed in England we. The actual number of images corresponding to the highest bar is also use whippet dog and whippet greyhound. also given for each dataset To further enlarge and diversify the candidate pool we. translate the queries into other languages 10 including. Chinese Spanish Dutch and Italian We obtain accurate. already far exceeds these two datasets In addition images. translations by WordNets in those languages 3 2 4 26. in these two datasets are largely uploaded or provided by. users or researchers of the dataset whereas ImageNet con. tains images crawled from the entire Internet The Lotus 3 2 Cleaning Candidate Images. Hill dataset is only available through purchase To collect a highly accurate dataset we rely on humans. to verify each candidate image collected in the previous step. 3 Constructing ImageNet for a given synset This is achieved by using the service of. Amazon Mechanical Turk AMT an online platform on, ImageNet is an ambitious project Thus far we have which one can put up tasks for users to complete and to. constructed 12 subtrees containing 3 2 million images Our get paid AMT has been used for labeling vision data 23. goal is to complete the construction of around 50 million With a global user base AMT is particularly suitable for. images in the next two years We describe here the method large scale labeling. we use to construct ImageNet shedding light on how prop. In each of our labeling tasks we present the users with. erties of Sec 2 can be ensured in this process, a set of candidate images and the definition of the target. synset including a link to Wikipedia We then ask the. 3 1 Collecting Candidate Images, users to verify whether each image contains objects of the. The first stage of the construction of ImageNet involves synset We encourage users to select images regardless of. collecting candidate images for each synset The average occlusions number of objects and clutter in the scene to. Y N Conf Conf 4 ImageNet Applications, 0 1 0 07 0 23 In this section we show three applications of ImageNet. User 1 Y Y Y, 1 0 0 85 0 69 The first set of experiments underline the advantages of hav.
User 2 N Y Y 1 1 0 46 0 49 ing clean full resolution images The second experiment. User 3 N Y Y 2 0 0 97 0 83,User 4 Y N Y, exploits the tree structure of ImageNet whereas the last ex. 0 2 0 02 0 12,User 5 Y Y Y 3 0 0 99 0 90, periment outlines a possible extension and gives more in. User 6 N N Y 2 1 0 85 0 68 sights into the data, Figure 7 Left Is there a Burmese cat in the images Six ran 4 1 Non parametric Object Recognition. domly sampled users have different answers Right The confi Given an image containing an unknown object we. dence score table for Cat and Burmese cat More votes are. would like to recognize its object class by querying similar. needed to reach the same degree of confidence for Burmese cat. images in ImageNet Torralba et al 24 has demonstrated. that given a large number of images simple nearest neigh. bor methods can achieve reasonable performances despite a. ensure diversity high level of noise We show that with a clean set of full. resolution images object recognition can be more accurate. While users are instructed to make accurate judgment. especially by exploiting more feature level information. we need to set up a quality control system to ensure this. accuracy There are two issues to consider First human We run four different object recognition experiments In. users make mistakes and not all users follow the instruc all experiments we test on images from the 16 common. tions Second users do not always agree with each other categories 7 between Caltech256 and the mammal subtree. especially for more subtle or confusing synsets typically at We measure classification performance on each category in. the deeper levels of the tree Fig 7 left shows an example the form of an ROC curve For each category the negative. of how users judgments differ for Burmese cat set consists of all images from the other 15 categories We. now describe in detail our experiments and results Fig 8. The solution to these issues is to have multiple users in. dependently label the same image An image is considered 1 NN voting noisy ImageNet First we replicate one. positive only if it gets a convincing majority of the votes of the experiments described in 24 which we refer. We observe however that different categories require dif to as NN voting hereafter To imitate the TinyIm. ferent levels of consensus among users For example while age dataset i e images collected from search engines. five users might be necessary for obtaining a good consen without human cleaning we use the original candi. sus on Burmese cat images a much smaller number is date images for each synset Section 3 1 and down. needed for cat images We develop a simple algorithm to sample them to 32 32 Given a query image we re. dynamically determine the number of agreements needed trieve 100 of the nearest neighbor images by SSD pixel. for different categories of images For each synset we first distance from the mammal subtree Then we perform. randomly sample an initial subset of images At least 10 classification by aggregating votes number of nearest. users are asked to vote on each of these images We then ob neighbors inside the tree of the target category. tain a confidence score table indicating the probability of an 2 NN voting clean ImageNet Next we run the same. image being a good image given the user votes Fig 7 right NN voting experiment described above on the clean. shows examples for Burmese cat and cat For each of ImageNet dataset This result shows that having more. remaining candidate images in this synset we proceed with accurate data improves classification performance. the AMT user labeling until a pre determined confidence. score threshold is reached It is worth noting that the con 3 NBNN We also implement the Naive Bayesian. fidence table gives a natural measure of the semantic diffi Nearest Neighbor NBNN method proposed in 5. culty of the synset For some synsets users fail to reach a to underline the usefulness of full resolution im. majority vote for any image indicating that the synset can ages NBNN employs a bag of features representa. not be easily illustrated by images 6 Fig 4 shows that our tion of images SIFT 15 descriptors are used in. algorithm successfully filters the candidate images result this experiment Given a query image Q with de. ing in a high percentage of clean images per synset scriptors di i 1 M for each object class. C we compute the query class distance DC, 7 The categories are bat bear camel chimp dog elk giraffe goat. 6 An alternative explanation is that we did not obtain enough suitable gorilla greyhound horse killer whale porcupine raccoon skunk zebra. candidate images Given the extensiveness of our crawling scheme this is Duplicates 20 per category with ImageNet are removed from the test. a rare scenario set,independent classifier,tree max classifier.
true positive rate,average AUC,NN voting clean ImageNet 0 5. NN voting noisy ImageNet 1 2 3 4 5 6 7 8 9,0 tree height. 0 0 2 0 4 0 6 0 8 1,false positive rate, Figure 9 Average AUC at each tree height level Performance. a average ROC comparison at different tree height levels between independently. trained classifiers and tree max classifiers The tree height of a. node is defined as the length of the longest path to its leaf nodes. All leaf nodes height is 1,true positive rate,true positive rate. method which we call the tree max classifier Imagine. NN voting clean ImageNet,NN voting clean ImageNet,NN voting noisy ImageNet.
NN voting noisy ImageNet you have a classifier at each synset node of the tree and you. 0 0 2 0 4 0 6 0 8 1 0 0 2 0 4 0 6 0 8 1, false positive rate false positive rate want to decide whether an image contains an object of that. b elk c killer whale synset or not The idea is to not only consider the classi. fication score at a node such as dog but also of its child. Figure 8 a Object recognition experiment results plotted in synsets such as German shepherd English terrier etc. ROC curves Each curve is the result of one of the four experi The maximum of all the classifier responses in this subtree. ments described in Section 4 1 It is an average of all ROC results becomes the classification score of the query image. of 16 object categories commonly shared between Caltech256 and. the mammal subtree Caltech256 images serve as testing images Fig 9 illustrates the result of our experiment on the. b c The ROC curve for elk and killer whale mammal subtree Note that our algorithm is agnostic to any. method used to learn image classifiers for each synset In. PM this case we use an AdaBoost based classifier proposed by. i 1 kdi dC 2 C, i k where di is the nearest neighbor of 6 For each synset we randomly sample 90 of the im. di from all the image descriptors in class C We order ages to form the positive training image set leaving the rest. all classes by DC and define the classification score of the 10 as testing images We form a common neg. as the minimum rank of the target class and its sub ative image set by aggregating 10 images randomly sam. classes The result shows that NBNN gives substan pled from each synset When training an image classifier. tially better performance demonstrating the advantage for a particular synset we use the positive set from this. of using a more sophisticated feature representation synset as well as the common negative image set excluding. available through full resolution images the images drawn from this synset and its child and parent. 4 NBNN 100 Finally we run the same NBNN experi synsets. ment but limit the number of images per category to We evaluate the classification results by AUC the area. 100 The result confirms the findings of 24 Per under ROC curve Fig 9 shows the results of AUC for. formance can be significantly improved by enlarging synsets at different levels of the hierarchy compared with. the dataset It is worth noting that NBNN 100 out an independent classifier that does not exploit the tree struc. performs NN voting with access to the entire dataset ture of ImageNet The plot indicates that images are easier. again demonstrating the benefit of having detailed fea to classify at the bottom of the tree e g star nosed mole. ture level information by using full resolution images minivan polar bear as opposed to the top of the tree e g. vehicles mammal artifact etc This is most likely due to. 4 2 Tree Based Image Classification stronger visual coherence near the leaf nodes of the tree. Compared to other available datasets ImageNet provides At nearly all levels the performance of the tree max. image data in a densely populated hierarchical structure classifier is consistently higher than the independent clas. Many possible algorithms could be applied to exploit a hi sifier This result shows that a simple way of exploiting. erarchical data structure e g 16 17 28 18 the ImageNet hierarchy can already provide substantial im. In this experiment we choose to illustrate the usefulness provement for the image classification task without addi. of the ImageNet hierarchy by a simple object classification tional training or model learning. P recis ion, Te hu Min tig Go L w he ta je t ba p mo g b tu y tr A p s c d s. xa m e lde y nx olf lic, n by a c e pe re y h ov in s ke a c h ic y c rma upp te a lt a me obb pa c. s lo a n iv a n r n ca ca d ou e r t le dil y ha l in es. ng Re te rria r nd lo irc hu,ho trie r ra ttle, Figure 10 Precision and recall of 22 categories from different.
levels of the hierarchy Precision is calculated by dividing the area. of correctly segmented pixels by the area of detected pixels Recall. is the fraction of relevant pixel area that is successfully detected. 4 3 Automatic Object Localization, ImageNet can be extended to provide additional infor. Figure 11 Samples of detected bounding boxes around different. mation about each image One such information is the spa. tial extent of the objects in each image Two application. areas come to mind First for training a robust object de. tection algorithm one often needs localized objects in dif. ferent poses and under different viewpoints Second hav. ing localized objects in cluttered scenes enables users to use. ImageNet as a benchmark dataset for object localization al. gorithms In this section we present results of localization. on 22 categories from different depths of the WordNet hier. archy The results also throw light on the diversity of images. in each of these categories, We use the non parametric graphical model described in. 14 to learn the visual representation of objects against a. global background class In this model every input im. age is represented as a bag of words The output is. a probability for each image patch to belong to the top. ics zi of a given category see 14 for details In or. der to annotate images with a bounding box we calcu. late the likelihood,P of each image patch given a category c. p x c i p x zi c p zi c Finally one bounding box, is put around the region which accumulates the highest like Figure 12 Left Average images and image samples of the de. lihood tected bounding boxes from the tusker and stealth aircraft cate. We annotated 100 images in 22 different categories of gories Right Average images and examples of three big clusters. the mammal and vehicle subtrees with bounding boxes after k means clustering see Sec 4 3 for detail Different view. around the objects of that category Fig 10 shows precision points and poses emerge in the tusker category The first row. shows tuskers in side view front view and in profile One cluster. and recall values Note that precision is low due to extreme. of aircraft images displays mostly planes on the ground. variability of the objects and because of small objects which. have hardly any salient regions,5 Discussion and Future Work.
Fig 11 shows sampled bounding boxes on different, classes The colored region is the detected bounding box Our future work has two goals. while the original image is in light gray, In order to illustrate the diversity of ImageNet inside 5 1 Completing ImageNet. each category Fig 12 shows results on running k means The current ImageNet constitutes 10 of the Word. clustering on the detected bounding boxes after converting Net synsets To further speed up the construction process. them to grayscale and rescaling them to 32 32 All average we will continue to explore more effective methods to eval. images including those for the entire cluster are created uate the AMT user labels and optimize the number of repe. with approximately 40 images While it is hard to iden titions needed to accurately verify each image At the com. tify the object in the average image of all bounding boxes pletion of ImageNet we aim to i have roughly 50 million. shown in the center due to the diversity of ImageNet the clean diverse and full resolution images spread over ap. average images of the single clusters consistently discover proximately 50K synsets ii deliver ImageNet to research. viewpoints or common poses communities by making it publicly available and readily ac. cessible online We plan to use cloud storage to enable effi References. cient distribution of ImageNet data iii extend ImageNet to 1 http www hunch net jl. include more information such as localization as described 2 The Chinese WordNet http bow sinica edu tw. in Sec 4 3 segmentation cross synset referencing of im 3 The Spanish WordNet http www lsi upc edu nlp. ages as well as expert annotation for difficult synsets and 4 A Artale B Magnini and S C Wordnet for italian and its use for. lexical discrimination In AI IA97 pages 16 19 1997. iv foster an ImageNet community and develop an online 5 O Boiman E Shechtman and M Irani In defense of nearest. platform where everyone can contribute to and benefit from neighbor based image classification In CVPR08 pages 1 8 2008. ImageNet resources 6 B Collins J Deng K Li and L Fei Fei Towards scalable dataset. construction An active learning approach In ECCV08 pages I 86. 5 2 Exploiting ImageNet 98 2008, 7 M Everingham L Van Gool C K I Williams J Winn and. We hope ImageNet will become a central resource for a A Zisserman The PASCAL Visual Object Classes Challenge 2008. broad of range of vision related research For the computer VOC2008 Results http www pascal network org. challenges VOC voc2008 workshop, vision community in particular we envision the following 8 L Fei Fei R Fergus and P Perona One shot learning of object. possible applications categories PAMI 28 4 594 611 April 2006. A training resource Most of today s object recognition 9 C Fellbaum WordNet An Electronic Lexical Database Bradford. Books 1998, algorithms have focused on a small number of common ob 10 R Fergus L Fei Fei P Perona and A Zisserman Learning object.
jects such as pedestrians cars and faces This is mainly due categories from google s image search In ICCV05 pages II 1816. to the high availability of images for these categories Fig 6 1823 2005. has shown that even the largest datasets today have a strong 11 M Fink and S Ullman From aardvark to zorro A benchmark for. mammal image classification IJCV 77 1 3 143 156 May 2008. bias in their coverage of different types of objects Ima 12 G Griffin A Holub and P Perona Caltech 256 object category. geNet on the other hand contains a large number of images dataset Technical Report 7694 Caltech 2007. for nearly all object classes including rare ones One inter 13 G Huang M Ramesh T Berg and E Learned Miller Labeled. faces in the wild A database for studying face recognition in uncon. esting research direction could be to transfer knowledge of. strained environments Technical Report 07 49 UMass 2007. common objects to learn rare object models 14 L J Li G Wang and L Fei Fei OPTIMOL automatic Online. A benchmark dataset The current benchmark datasets Picture collecTion via Incremental MOdel Learning In CVPR07. in computer vision such as Caltech101 256 and PASCAL pages 1 8 2007. 15 D Lowe Distinctive image features from scale invariant keypoints. have played a critical role in advancing object recognition IJCV 60 2 91 110 November 2004. and scene classification research We believe that the high 16 M Marszalek and C Schmid Semantic hierarchies for visual object. quality diversity and large scale of ImageNet will enable recognition In CVPR07 pages 1 7 2007. it to become a new and challenging benchmark dataset for 17 M Marszalek and C Schmid Constructing category hierarchies for. visual recognition In ECCV08 pages IV 479 491 2008. future research 18 D Nister and H Stewenius Scalable recognition with a vocabulary. Introducing new semantic relations for visual modeling tree In CVPR06 pages II 2161 2168 2006. Because ImageNet is uniquely linked to all concrete nouns 19 P Phillips H Wechsler J Huang and P Rauss The feret database. and evaluation procedure for face recognition algorithms IVC. of WordNet whose synsets are richly interconnected one 16 5 295 306 April 1998. could also exploit different semantic relations for instance 20 E Rosch and B Lloyd Principles of categorization In Cognition. to learn part models To move towards total scene under and categorization pages 27 48 1978. standing it is also helpful to consider different depths of 21 B Russell A Torralba K Murphy and W Freeman Labelme. A database and web based tool for image annotation IJCV 77 1. the semantic hierarchy 3 157 173 May 2008, Human vision research ImageNet s rich structure and 22 J Shotton J Winn C Rother and A Criminisi Textonboost Joint. dense coverage of the image world may help advance the appearance shape and context modeling for multi class object recog. nition and segmentation In ECCV06 pages I 1 15 2006. understanding of the human visual system For example 23 A Sorokin and D Forsyth Utility data annotation with amazon me. the question of whether a concept can be illustrated by im chanical turk In InterNet08 pages 1 8 2008. ages is much more complex than one would expect at first 24 A Torralba R Fergus and W Freeman 80 million tiny images A. Aligning the cognitive hierarchy with the visual hierarchy large data set for nonparametric object and scene recognition PAMI. 30 11 1958 1970 November 2008, also remains an unexplored area 25 L von Ahn and L Dabbish Labeling images with a computer game. In CHI04 pages 319 326 2004, Acknowledgment 26 P Vossen K Hofmann M de Rijke E Tjong Kim Sang and K De. schacht The Cornetto database Architecture and user scenarios In. The authors would like to thank Bangpeng Yao Hao Su Barry Proceedings DIR 2007 pages 89 96 2007. Chai and anonymous reviewers for their helpful comments WD is 27 B Yao X Yang and S Zhu Introduction to a large scale general. supported by Gordon Wu fellowship RS is supported by the ERP purpose ground truth database Methodology annotation tool and. and Upton fellowships KL is funded by NSF grant CNS 0509447 benchmarks In EMMCVPR07 pages 169 183 2007. 28 A Zweig and D Weinshall Exploiting object hierarchy Combining. and by research grants from Google Intel Microsoft and Yahoo models from different category levels In ICCV07 pages 1 8 2007. LFF is funded by research grants from Microsoft and Google.

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