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Comprehensive Overview of Content-Based Image Retrieval Techniques and Their Challenges

This overview explores the field of Content-Based Image Retrieval (CBIR), discussing its academic significance and real-world applications. The text highlights the challenges faced in CBIR, including problems of object recognition, feature extraction, and evaluation metrics. It examines various research efforts and methodologies, such as MPEG-7 descriptors and the Bag of Features approach. By analyzing the evolution of image retrieval systems and their performance metrics, this work provides valuable insights into the complexities of organizing and retrieving digital images effectively.

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Comprehensive Overview of Content-Based Image Retrieval Techniques and Their Challenges

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Presentation Transcript

  1. Overview of Image Retrieval Hui-Ying Wang

  2. Reference • Smeulders, A. W., Worring, M., Santini, S., Gupta, A., , and Jain, R. 2000. “Content-based image retrieval at the end of the early years.” IEEE Trans. Pattern Analysis and Machine Intelligence 22, 12, 1349–1380. • R. Datta, D. Joshi, J. Li and J. Z. Wang, ”Image Retrieval: Ideas, Influences, and Trends of the New Age,” ACM Computing Surveys, 2008, to appear. • CVPR 2007 short course: Recognizing and Learning Object Categories http://people.csail.mit.edu/torralba/shortCourseRLOC/index.html

  3. Outline • Motive • Academic and real world • Difficulties and problem model • Evaluation metrics

  4. Outline • Motive • Academic and real world • Difficulties and problem model • Evaluation metrics

  5. Motive • Popular electronic device • Digital camera • By-product • Digital photos • Need • Organization • Key: filenames? dates?

  6. Outline • Motive • Academic and real world • Difficulties and problem model • Evaluation metrics

  7. Publications in CBIR

  8. Search engines Google Image (2.2 b) Picsearch (1.7 b) Yahoo! Images (1.6 b) AltaVista Ask Images Online albums Flickr Riya Webshots Shopping like Real-world system

  9. Search engines Google Image (2.2 b) Picsearch (1.7 b) Yahoo! Images (1.6 b) AltaVista Ask Images Online albums Flickr Riya Webshots Shopping like Real-world system

  10. Google Images

  11. Google Image Labeler

  12. Picsearch

  13. Yahoo! Images

  14. AltaVista

  15. Ask Images

  16. Search engines Google Image (2.2 b) Picsearch (1.7 b) Yahoo! Images (1.6 b) AltaVista Ask Images Online albums Flickr Riya Webshots Shopping like Real-world system

  17. Flickr

  18. Webshots

  19. Riya

  20. Search engines Google Image (2.2 b) Picsearch (1.7 b) Yahoo! Images (1.6 b) AltaVista Ask Images Online albums Flickr Riya Webshots Shopping like Real-world system

  21. like

  22. Outline • Motive • Academic and real world • Difficulties and problem model • Evaluation metrics

  23. Challenges view point variation occlusion scale deformation illumination

  24. Goal computer vision real object sensory gap digital record interpretation semantic gap extraction human vision

  25. Core problems • How to describe an image • How to assess the similarity

  26. Some features • Global features • MPEG-7 • Color Layout Descriptor • Edge Histogram Descriptor • Homogeneous Texture Descriptor • Summarizing local features • Bag of Features

  27. Some features • Global features • MPEG-7 • Color Layout Descriptor • Edge Histogram Descriptor • Homogeneous Texture Descriptor • Summarizing local features • Bag of Features

  28. Color Layout Descriptor - Presentation • MPEG-7

  29. Color Layout Descriptor - Similarity

  30. Some features • Global features • MPEG-7 • Color Layout Descriptor • Edge Histogram Descriptor • Homogeneous Texture Descriptor • Summarizing local features • Bag of Features

  31. Edge Histogram Descriptor - Presentation • MPEG-7

  32. Edge Histogram Descriptor - Similarity

  33. Some features • Global features • MPEG-7 • Color Layout Descriptor • Edge Histogram Descriptor • Homogeneous Texture Descriptor • Summarizing local features • Bag of Features

  34. Homogeneous Texture Descriptor - Presentation Fourier transform Gabor function e: log-scaled sum of the squares of Gabor-filtered Fourier transform coefficients d: log-scaled standard deviation of the squares of Gabor-filtered Fourier transform coefficients Human Vision System fDC: mean deviation fSD: standard deviation

  35. Homogeneous Texture Descriptor - Similarity

  36. Some features • Global features • MPEG-7 • Color Layout Descriptor • Edge Histogram Descriptor • Homogeneous Texture Descriptor • Summarizing local features • Bag of Features

  37. Local feature • Detected keypoints • spatial relationship • fully independent (ex: bag of features) • fully connected

  38. Bag of Features

  39. Outline • Motive • Academic and real world • Difficulties and problem model • Evaluation metrics

  40. Evaluation (1/2) • Standard • Precision • # of retrieved positive images / # of total retrieved images • Recall • # of retrieved positive images / # of total positive images

  41. Evaluation (1/2) • When number of retrieved images increase • Recall ↑ Precision ↓ • Average precision (AP) • The area under the precision-recall curve for a query 1 AP precision 1 recall

  42. The end ~ Thank you

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