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Descriptive Semantic Image Retrieval

Descriptive Semantic Image Retrieval. David Norton Derral Heath. Motivation. Retrieve an image based on a descriptive query: “Find me an image that is red, dark, scary, and beautiful”. Content-Based Image Retrieval. Retrieve an image strictly from image features color texture shape

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Descriptive Semantic Image Retrieval

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  1. Descriptive Semantic Image Retrieval David Norton Derral Heath

  2. Motivation • Retrieve an image based on a descriptive query: • “Find me an image that is red, dark, scary, and beautiful”

  3. Content-Based Image Retrieval • Retrieve an image strictly from image features • color • texture • shape • General semantic based image retrieval is hard • “Find me a picture of a piranha”

  4. Emotional Semantic Image Retrieval • Query images matching emotional words or word-pairs • “Find me a happy picture” • Usually adjectives • Usually a small orthogonal subset of terms • Query via single words (or pairs)

  5. Descriptive Semantic Image Retrieval • Open to all descriptive words • Query via any number of words • “Find me an image that is red, dark, scary, and beautiful”

  6. Three Components • Extraction of image features • Semantic representation of image • Mapping between visuals and language

  7. Extraction of Image Features • Color (12) • Average RGB values • Color count • Texture (50) • Entropy • Shape (10) • Eccentricity

  8. Extraction of Image Features

  9. Extraction of Image Features

  10. Semantic Representation of Image • How do we obtain a description? • What is a descriptive word? • What are the features?

  11. User Input Interface

  12. Bright has 11 Senses • 1. (17) bright -- (emitting or reflecting light readily or in large amounts; "the sun was bright and hot"; "a bright sunlit room") • 2. (6) bright, brilliant, vivid -- (having striking color; "bright dress"; "brilliant tapestries"; "a bird with vivid plumage") • 3. (5) bright, smart -- (characterized by quickness and ease in learning; "some children are brighter in one subject than another"; "smart children talk earlier than the average") • 4. (3) bright -- (having lots of light either natural or artificial; "the room was bright and airy"; "a stage bright with spotlights") • 5. (1) bright, burnished, lustrous, shining, shiny -- (made smooth and bright by or as if by rubbing; reflecting a sheen or glow; "bright silver candlesticks"; "a burnished brass knocker"; "she brushed her hair until it fell in lustrous auburn waves"; "rows of shining glasses"; "shiny black patents") • 6. (1) bright -- (splendid; "the bright stars of stage and screen"; "a bright moment in history"; "the bright pageantry of court") • 7. undimmed, bright -- (not made dim or less bright; "undimmed headlights"; "surprisingly the curtain started to rise while the houselights were still undimmed") • 8. bright, brilliant -- (clear and sharp and ringing; "the bright sound of the trumpet section"; "the brilliant sound of the trumpets") • 9. bright -- (characterized by happiness or gladness; "bright faces"; "all the world seems bright and gay") • 10. bright, shining, shiny, sunshiny, sunny -- (abounding with sunlight; "a bright sunny day"; "one shining morning"- John Muir; "when it is warm and shiny") • 11. bright, promising -- (full or promise; "had a bright future in publishing"; "the scandal threatened an abrupt end to a promising political career")

  13. Narrowing Down the Feature Space • Interface: • Adjectives from WordNet • Restrict characters • Reduce available senses

  14. Narrowing Down the Feature Space • Post Processing: • Use Synsets • Frequent synsets • Fit ORM ontology lexicons

  15. Image ORM Ontology

  16. Mapping between visuals and language • Series of Neural Networks • Bayes Net • Fuzzy Logic

  17. Evaluation • Let machine label images • Let humans label images • Let different humans evaluate machine and human labels • Compare evaluations

  18. Related Work • Aesthetic Visual Quality Assessment of Paintings (2009) • Congcong Li and Tsuhan Chen • Labeled impressionistic style landscape paintings as ‘high’ or ‘low’ quality using machine learning. • Algorithmic Inferencing of Aesthetics and Emotion in Natural Images: An Exposition (October 2008) • Ritendra Datta, Jia Li, and James Z. Wang • Overview of research involving predicting the quality class, score, and emotional label of photographs.

  19. Related Work • A Survey on Emotional Semantic Image Retrieval (2008) • Weining Wang and Qianhua He • Surveys ongoing Emotional Semantic Image Retrieval research. • Image Retrieval by Emotional Semantics: A Study of Emotional Space and Feature Extraction (October 2006) • Wang Wei-ning, Yu Ying-lin, and Jiang Sheng-ming • Labeled paintings with 12 emotional word pairs. Psychological research involved in choosing word pairs.

  20. Further Motivation • Augment the study of human perception and cognition. • Establish a linguistic-visual foundation for an artificially creative artist.

  21. Questions?

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