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Pictures and Words,[object Object]
Vision and language in human brain,[object Object],Language,[object Object],Vision,[object Object],Wernicke,[object Object],Area,[object Object],Broca,[object Object],Area,[object Object],PPA,[object Object],LOC,[object Object],V1,[object Object],FFA,[object Object]
Vision and language in human brain,[object Object],figure modified from: http://www.colorado.edu/intphys/Class/IPHY3730,[object Object]
Vision and language in human brain,[object Object],?,[object Object],(Translation: “This is not a pipe.”),[object Object],figure modified from: http://www.colorado.edu/intphys/Class/IPHY3730,[object Object]
Iccv2009 recognition and learning object categories   p2 c03 - objects and annotations
Iccv2009 recognition and learning object categories   p2 c03 - objects and annotations
What can you see in a glance of a scene?,[object Object],Fei-Fei, Iyer, Koch, Perona, JoV, 2007,[object Object]
PT = 27ms,[object Object],This was a picture with some dark sploches in it. Yeah. . .that's about it. (Subject: KM),[object Object],PT = 40ms,[object Object],I think I saw two people on a field. (Subject: ,[object Object],RW) ,[object Object],PT = 67ms,[object Object],Outdoor scene. There were some kind of animals, maybe dogs or horses, in the middle of the picture. It looked like they were running in the middle of a grassy field. (Subject: IV) ,[object Object],PT = 500ms,[object Object],Some kind of game or fight. Two groups of two men? The foregound pair looked like one was getting a fist in the face. Outdoors seemed like because i have an impression of grass and maybe lines on the grass? That would be why I think perhaps a game, rough game though, more like rugby than football because they pairs weren't in pads and helmets, though I did get the impression of similar clothing. maybe some trees? in the background. (Subject: SM),[object Object],PT = 107ms,[object Object],two people, whose profile was toward me. looked like they were on a field of some sort and engaged in some sort of sport (their attire suggested soccer, but it looked like there was too much contact for that). (Subject: AI) ,[object Object],Fei-Fei, Iyer, Koch, Perona, JoV, 2007,[object Object]
Section outline,[object Object],Early “pictures and words” work,[object Object],Content-based retrieval,[object Object],Beyond nouns, towards total scene annotation,[object Object]
“Pictures and words”,[object Object],Barnard, Duygulu, de Freitas, Forsyth, Blei, Jordan, Matching words and pictures, JMLR, 2003,[object Object],Duygulu, Barnard, de Freitas, Forsyth, Object Recognition as Machine Translation: Learning a lexicon for a fixed image vocabulary , ECCV, 2003,[object Object],Blei & Jordan, Modeling annotated data, ACM SIGIR, 2003,[object Object],Chang, Goh, Sychay, & Wu, Soft annotation using Bayes point machines, IEEE Transactions on Circuits and Systems for Video Technology, 2003,[object Object],Goh, Chang, & Cheng, Ensemble of SVM-based classifiers for annotation, 2003,[object Object],….,[object Object]
[object Object]
Images are clustered based on priors over concepts.
Learning determines localized concepts models from global annotations.
Addresses the correspondence problem
One possible assumption: concept models simultaneously generate both a word and blob  sun,[object Object],sun,[object Object],sky,[object Object],water,[object Object],waves,[object Object],Barnard et al. JMLR, 2005,[object Object],Slide courtesy of Kobus Barnard (1 hour ago!),[object Object]
[object Object]
Chose an image cluster by p(c)
Chose multimodal concept clusters using p(s|c)
From each multimodal cluster, sample a Gaussian for blob features, p(b|s), and a multinomial for words, p(w|s)
(Skip with some probability to account for mismatched numbers of words and blobs)
For a given correspondence*sun,[object Object],sun,[object Object],sky,[object Object],water,[object Object],waves,[object Object],Barnard et al. JMLR, 2005,[object Object],Slide courtesy of Kobus Barnard (1 hour ago!),[object Object]
Barnard et al. JMLR, 2005,[object Object]
Section outline,[object Object],Early “pictures and words” work,[object Object],Content-based retrieval,[object Object],Beyond nouns, towards total scene annotation,[object Object]
Content-based retrieval,[object Object],Elegance,[object Object],Love,[object Object],Symmetry,[object Object],Flower,[object Object],Petals,[object Object],Tower,[object Object],France,[object Object],Rose,[object Object],Corolla,[object Object],Australian Floribunda Rose,[object Object],EiffelTower,[object Object],Paris,[object Object],Slide courtesy of RitendraDatta, Jia Li, James Z. Wang,[object Object]
Literature – MANY!!!,[object Object],A. W. Smeulders, M. Worring, S. Santini, A. Gupta, R. Jain, Content-Based Image Retrieval at the End of the Early Years, IEEE Trans. Pattern Analysis and Machine Intelligence , 22(12):1349-1380, 2000. ,[object Object],R. Datta, D. Joshi, J. Li, and J. Z. Wang, Image Retrieval: Ideas, Influences, and Trends of the New Age, ACM Computing Surveys, vol. 40, no. 2, pp. 5:1-60, 2008.,[object Object]
Try out Alipr (www.alipr.com),[object Object]
Try out Alipr (www.alipr.com),[object Object]
Automatic Image Annotation: ALIP,[object Object],Slide courtesy ofRitendraDatta, Jia Li, James Z. Wang,[object Object]
Automatic Image Annotation: ALIP,[object Object],Slide courtesy ofRitendraDatta, Jia Li, James Z. Wang,[object Object]
Automatic Image Annotation: ALIP,[object Object],2D-MHMM: Two-dimensional multi-resolution hidden Markov model,[object Object],Slide courtesy ofRitendraDatta, Jia Li, James Z. Wang,[object Object]
Automatic Image Annotation: ALIP,[object Object],Annotation Process,[object Object],[object Object]
Salient words appearing in the classification favored moreFood, indoor, cuisine, dessert,[object Object],Building, sky, lake, landscape, Europe, tree,[object Object],Snow, animal, wildlife, sky, cloth, ice, people,[object Object],Slide courtesy ofRitendraDatta, Jia Li, James Z. Wang,[object Object]
Section outline,[object Object],Early “pictures and words” work,[object Object],Content-based retrieval,[object Object],Beyond nouns, towards total scene annotation,[object Object],Propositions,[object Object],A. Gupta and L. S. Davis, Beyond Nouns: Exploiting prepositions and comparative adjectives for learning visual classifiers, ECCV, 2008,[object Object],Objects, scenes, activities,[object Object],L.-J. Li and L. Fei-Fei. What, where and who? Classifying event by scene and object recognition. ICCV, 2007,[object Object],L.-J. Li, R. Socher and L. Fei-Fei. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework. CVPR, 2009,[object Object]
Section outline,[object Object],Early “pictures and words” work,[object Object],Content-based retrieval,[object Object],Beyond nouns, towards total scene annotation,[object Object],Propositions,[object Object],A. Gupta and L. S. Davis, Beyond Nouns: Exploiting prepositions and comparative adjectives for learning visual classifiers, ECCV, 2008,[object Object],Objects, scenes, activities,[object Object],L.-J. Li and L. Fei-Fei. What, where and who? Classifying event by scene and object recognition. ICCV, 2007,[object Object],L.-J. Li, R. Socher and L. Fei-Fei. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework. CVPR, 2009,[object Object]
Gupta & Davis, EECV, 2008,[object Object],“Beyond nouns”,[object Object]
“Beyond nouns”,[object Object],Gupta & Davis, EECV, 2008,[object Object]
Gupta & Davis, EECV, 2008,[object Object]
Section outline,[object Object],Early “pictures and words” work,[object Object],Content-based retrieval,[object Object],Beyond nouns, towards total scene annotation,[object Object],Propositions,[object Object],A. Gupta and L. S. Davis, Beyond Nouns: Exploiting prepositions and comparative adjectives for learning visual classifiers, ECCV, 2008,[object Object],Objects, scenes, activities,[object Object],L.-J. Li and L. Fei-Fei. What, where and who? Classifying event by scene and object recognition. ICCV, 2007,[object Object],L.-J. Li, R. Socher and L. Fei-Fei. Towards Total Scene Understanding:Classification, Annotation and Segmentation in an Automatic Framework. CVPR, 2009,[object Object]
What, where and who? Classifying events by scene and object recognition,[object Object],L-J Li & L. Fei-Fei, ICCV 2007,[object Object]
scene pathway,[object Object],object pathway,[object Object],event,[object Object],PFC,[object Object],“where” pathway,[object Object],“what” pathway,[object Object],L.-J. Li & L. Fei-Fei ICCV 2007,[object Object]
scene pathway,[object Object],“Polo Field”,[object Object],Fei-Fei & Perona, CVPR, 2005,[object Object],L.-J. Li & L. Fei-Fei ICCV 2007,[object Object]
O= ‘horse’,[object Object],object pathway,[object Object],G. Wang & L. Fei-Fei, CVPR, 2006,[object Object],L.-J. Li , G. Wang & L. Fei-Fei, CVPR, 2007,[object Object],L. Cao & L. Fei-Fei, ICCV, 2007,[object Object],L.-J. Li & L. Fei-Fei ICCV 2007,[object Object]
The 3W stories,[object Object],what,[object Object],who,[object Object],where,[object Object],L.-J. Li & L. Fei-Fei ICCV 2007,[object Object]

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