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Clustering Art & Learning the Semantics of Words and Pictures Manigantan Sethuraman

Clustering Art & Learning the Semantics of Words and Pictures Manigantan Sethuraman. Key Applications. Auto Annotation Given image generate associated words. Auto Illustration Given words generate associated images. Sounds Familiar Isn’t It ?. Key Ideas.

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Clustering Art & Learning the Semantics of Words and Pictures Manigantan Sethuraman

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  1. Clustering Art & Learning the Semantics of Words and Pictures Manigantan Sethuraman

  2. Key Applications • Auto Annotation • Given image generate associated words. • Auto Illustration • Given words generate associated images. • Sounds Familiar Isn’t It ?

  3. Key Ideas • Joint Probability Distribution • Complete Sense is conveyed by considering words and images together. • Hierarchical Model • Going from General to Specific. • Allowing shared use of information. • Providing a search path. • Clustering • Basically grouping, Images or Regions ?? • Soft (Membership is distributed)

  4. Joint Prob. Distr. -> Text Only

  5. Joint Prob. Distr. -> Images Only

  6. Joint Prob. Distr. –> Words & Images

  7. Each Node has a probability of generating a word/ image w.r.t the document under consideration. Cluster defines the path. Cluster,Level identifies the node. Hierarchical Model

  8. Associated Math • P(c | d) – Probability of cluster given the document. • P(L | c,d) – Probability of the level given the cluster and document. • P(i | l,c) – Probability of item given the level and cluster. • P(L | c,d) can be roughly represented by their average P(L | c). • Model 1 uses the document specific value. • Model 2 uses the average value.

  9. Auto Annotation • Generate words for a given image • Consider the probability of the image belonging to the current cluster. • Consider the probability of the items in the image being generated by the nodes at various levels in the path associated to the current cluster. • Work the above out for all clusters. • We are computing the probability that an image emits a proposed word, given the observed segments, B:

  10. Auto Illustration

  11. Is E-M Used ? • E-M is used to train and obtain the hidden information. • Clustering • Probability of a document d being in the cluster c • Image-Word Correlation • Probability that Item i of Document d was generated at level L.

  12. Word Sense Disambiguation • Semantic Hierarchies • Bank -> Financial Institution -> Institution -> Organization. • Bank -> slope -> geological formation -> natural object. • Word Sense defined by the path to the root. • Rather than considering the word as an item, consider the word-sense as an item • Six closest words for each occurrence of a word used to disambiguate its sense. • For each word the sense which has the largest hypernym (IS_A) sense in common with the neighboring words is chosen.

  13. Questions & Discussion • Relationship between Object Recognition paper and this paper… • Handling Noise ? • Irrelevant descriptions for images • Dependence on semantically meaningful segmentation…

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