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IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK Hayati CAM Ozge CAVUS

IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK Hayati CAM Ozge CAVUS. Outline. Question: What is Content Based Image Retrieval? Recent Work on CBIR Our Approach Evaluation Summary. CBIR. Large quantities of multimedia data is used in archives

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IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK Hayati CAM Ozge CAVUS

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  1. IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK Hayati CAM Ozge CAVUS Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  2. Outline • Question: • What is Content Based Image Retrieval? • Recent Work on CBIR • Our Approach • Evaluation • Summary Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  3. CBIR • Large quantities of multimedia data is used in archives • Traditional way: Using keywords in IR(Image Retrieval) • Problems: • Annotation is very difficult • Keywords may be insufficient to represent the contents of the images • Keywords are user dependent Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  4. CBIR Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  5. Recent Work • Extracting global low-level features (texture or color) from images • Problem: limited in capability of deriving higher semantic meanings of the images Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  6. Recent Work • Partitioning images into nonoverlapping grid cell • Problem: Grids are not meaningful regions Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  7. Our Approach Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  8. Our Approach • Image Segmentation • Codebook Construction • Image Representation by using Posterior Class Probability Values • Content Based Image Retrieval with Relevance Feedback Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  9. Dataset • TRECVID 2005 dataset • 29832 video shots • Contain approximately 20 different classes • exp: mountain, seaside, urban, sports … Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  10. Image Segmentation Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  11. Image Segmentation • Cluster the RGB color values of the pixels by k-means Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  12. Image Segmentation • Smooth the regions by combined classifier approach Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  13. Codebook Construction Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  14. Image Representation • Calculate region k=1000 bins histograms for each image Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  15. Image Representation Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  16. Image Representation Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  17. Relevance Feedback • At the first iteration images are ranked by distances to the query image • After each iteration user labels the images as relevant and irrelevant • The new result are retrieved according to the user feedback Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  18. Content Based Image Retrieval Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  19. Relevance Feedback • Assign a weight value w to each class probability value • The weights are assigned uniformly in the first iteration. Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  20. Relevance Feedback • Given two images: • Distances between the corresponding probability terms are computed • di = distance between the ith probability values of two images where i=1, …, c • These distances are combined as • d = ∑ wi di Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  21. Relevance Feedback • Given the positive and negative examples, for a probability term being significant for a particular query: • Distances for the corresponding probability values for relevant images must usually be similar (hence, a small variance), • Distances between the probability values for relevant images and irrelevant images must usually be different (hence, a large variance). Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  22. Relevance Feedback • Weights are computed as: std(distances of ith probability term between relevant and irrelevant images) Wi = std(distances of ith probability term between relevant images) Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  23. Evaluation • Yao’s formula for cluster validation • ntr > nt • Why do we need this? • Better Clustering -> Better Probability Values -> Better Retrieval Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  24. Evaluation • Precision-Recall Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  25. Summary • Steps of Our Approach • Image Segmentation • Codebook Construction • Image Representation by probabilities • CBIR with Relevance Feedback Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

  26. THANK YOU Image Retrieval With Relevant Feedback Hayati Cam & Ozge Cavus

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