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Clustering for Word and Picture Semantics Learning

Explore the fusion of words and images in a hierarchical model, leveraging joint probability distributions to auto-annotate images and generate illustrations. Enhance word sense disambiguation and image-word correlations through clustering.

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Clustering for Word and Picture Semantics Learning

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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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