Multimedia analysis and retrieval
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1 Multimedia analysis and retrieval from the masses, and for the masses Lexing Xie IBM T J Watson Research Center xlx@us.ibm.com ICIP-MIR workshop, Oct 12, IBM Research
2 multimedia information analysis need to scale up Multimodal, multi-source, real-time streams Data Information; Storage Memory? new broadcast ten channels, one year 1,300 GB, 1,830 hrs surveillance one camera, one year 3,556 GB, 8,760 hrs one year of digital life 200 GB, ~10 3 hrs online media collections?? billion 2008 IBM 2
3 challenges in three fronts Data Semantics? G'day Bonjour Tere Salut Përshëndetje Szia Labdien Sawubona Hoi Shalom Ciao Salaam People 2008 IBM 3
4 scene completion initial experiments with the GIST descriptor on ten thousand images were very discouraging however increasing the dataset to one million yielded a qualitative leap. [Hays and Efros SIGGRAPH07] 2008 IBM 4
5 visual concept detection Task: score each image in terms of a set of pre-defined visual concepts IBM 5
6 random subspace bagging Features Training Examples SVM 1 SVM 2 Classifiers [Yan, Tesic and Smith KDD07] 2008 IBM 6
7 two approaches for scaling up Large number of models vs. large models data scalability can happen in many different levels job parallelization and decomposition is the key N n n N p N p [Yan et. al. KDD 07] N [Chang et. al. NIPS 07] 2008 IBM 7
8 the multiple semantics space concepts coexist can we use concept colltion to improve accuracy efficiency anchor? studio? soccer? outdoor? female person? snow? building? 2008 IBM 8
9 [Caltech256] 2008 IBM 9
10 large lexicon helps concept detection retrieval Concept detection performance Search performance [Xie, Yan and Yang 08] [Snoek 07] Precision Average Precision Recall simple generative models perform robustly 2008 IBM 10
11 outlook on concept relations semantics have natural hierarchies a picture is worth thousand words there are often more than one label per image the observables (label scores) are noisy reflections of the true semantics long-tail is everywhere how to learn from few examples or no examples? 2008 IBM 11
12 Outline Data Semantics G'day Bonjour Tere Salut Përshëndetje Szia Labdien Sawubona Hoi Shalom Ciao Salaam People 2008 IBM 12
13 Multimedia is Social {image, taken date, upload date, location, tags, author, comments, } Flickr image clusters with tag coocurrence. landmark image clusters with tag+geo filtering [Kennedy and Naaman, WWW 2008] 2008 IBM 13
14 contextual wisdom when An event refers to a real-world occurrence, spread over space and time Event context refers to the set of attributes that help one understand the semantics Images / Who / Where / When / What / Why / How Context is application dependent Ubiquitous computing location, identity and time are main considerations Systems the virtual environment required to suspend a running program Language the relevant constraints of the communicative situation that influence language use, language variation and discourse [Westermann / Jain 2007] author image where who what 2008 IBM 15
15 three aspects of social context similarity co-occurrence trust vectors context C e C f analogous Ae Af Images (feature) Where (location) path based e f = ωi 1 2 i= 1 ES( e, e ) s( e, e ; i) What (activity) Who (people) k t = α A t + (1 α ) p, When (time) M c k = i= 1 k ( a, b) M ( a, b) c [joint work with Hari Sundaram, Bagshree Shevade, Amit Zunjaward in ASU] 2008 IBM 16
16 recommending tags similarity co-occurrence trust vectors global personal social query who where when what image event 1. Compute the social network trust vector (t) for the current user. 2. Compute the trusted global co-occurrence matrix for all tuples. 3. Iterate: M c N = i = 1 k k ( a, b) t ( i) M ( a, b), y = M x + q, c x = M y + q, s c 2008 IBM 17
17 Event-based archival system SVM sky diving Social Network based method fun Facets SVM CM (network) H M X U H M Who When Where H Hits What M Misses X Unknown Events U Undecidable tag recommendation results on personal collections 2008 IBM 18
18 Summary MIR needs to scale up for the masses there are three main aspects in this challenge: data, semantics and people Images (feature) Where (location) What (activity) Who (people) When (time) 2008 IBM 19
19 Thank you IBM 20
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