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H euristic Pre-Clustering Relevance Feedback in Attention -Based Image Retrieval

H euristic Pre-Clustering Relevance Feedback in Attention -Based Image Retrieval. Wan-Ting Su , Wen-Sheng Chu and Jenn-Jier James Lien Speaker: Wen-Sheng Chu Robotics Lab. CSIE NCKU. Query Image. Positive Feedback. Negative Feedback. Heuristic Pre-Clustering View.

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H euristic Pre-Clustering Relevance Feedback in Attention -Based Image Retrieval

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  1. Heuristic Pre-Clustering Relevance Feedback in Attention-Based Image Retrieval Wan-Ting Su, Wen-Sheng Chu and Jenn-Jier James Lien Speaker: Wen-Sheng Chu Robotics Lab. CSIE NCKU

  2. Query Image Positive Feedback Negative Feedback Heuristic Pre-Clustering View User can change the positive group number on his/her own User can revise the clustering results manually Result View System Interface System Interface Robotics Lab, CSIE NCKU

  3. System Overview Offline Module : Attention-Based Image Retrieval Feature Extraction from Attended View Wavelet Transformation Attended View Extraction Image Database Low-Low Subband Query Image User Feedback? Ranking by Euclidean Distance Best Matches No END Yes Ranking by GBDA Learning PCA HeuristicPre-clustering UserRe-clustering VQ Online Module : Heuristic Pre-Clustering Relevance Feedback Robotics Lab, CSIE NCKU

  4. attention center Gaussian distance contrast value of pixel p at image location (i, j) neighborhood of pixel (i, j) Wavelet andAttended View Extraction • To reduce the computational cost • Contrast extraction is applied to the wavelet coefficient in the LL-subband. Got saliency map! Robotics Lab, CSIE NCKU

  5. System Overview Offline Module : Attention-Based Image Retrieval Feature Extraction from Attended View Wavelet Transformation Attended View Extraction Image Database Low-Low Subband Query Image User Feedback? Ranking by Euclidean Distance Best Matches No END Yes Ranking by GBDA Learning PCA HeuristicPre-clustering UserRe-clustering VQ Online Module : Heuristic Pre-Clustering Relevance Feedback Robotics Lab, CSIE NCKU

  6. Features Dimension Color mean, standard deviation and skew in HSV space 9 Standard deviation of the wavelet coefficients in 10 pyramid de-correlated sub-bands 10 13 statistical elements extracted from the edge map such as max fill time, max fork count, etc. 13 Visual Features Extraction • Table1. 32 low-level visual features Robotics Lab, CSIE NCKU

  7. System Overview Offline Module : Attention-Based Image Retrieval Feature Extraction from Attended View Wavelet Transformation Attended View Extraction Image Database Low-Low Subband Got features! Query Image User Feedback? Ranking by Euclidean Distance Best Matches No END Yes Ranking by GBDA Learning PCA HeuristicPre-clustering UserRe-clustering VQ Online Module : Heuristic Pre-Clustering Relevance Feedback Robotics Lab, CSIE NCKU

  8. Pre-Clustering • Principal Component Analysis (PCA) + • Vector Quantization algorithm (VQ) Robotics Lab, CSIE NCKU

  9. User Re-clustering Result User Re-clustering User Re-clustering System Pre-clustering Result Robotics Lab, CSIE NCKU

  10. System Overview Offline Module : Attention-Based Image Retrieval Feature Extraction from Attended View Wavelet Transformation Attended View Extraction Image Database Low-Low Subband Query Image User Feedback? Ranking by Euclidean Distance Best Matches No END Yes Ranking by GBDA Learning PCA HeuristicPre-clustering UserRe-clustering VQ Online Module : Heuristic Pre-Clustering Relevance Feedback Robotics Lab, CSIE NCKU

  11. Negative Samples Positive Samples Bouquets of Flowers Single Flower Re-weighting Scheme • Group-Based Discriminant Analysis (GBDA) • Multiple positive and multiple negative classes • Clustering each positive class • Scattering the negative example away from each positive class Robotics Lab, CSIE NCKU

  12. mi : the mean of the ith positive classCi c: the number of positive groups D : a set of negative examples GBDA Sw : the sum of the within-class scatter matrix of the positive groups SPN is the sum of between-class scatter matrices of positive-to-negative Robotics Lab, CSIE NCKU

  13. Experiment Result (1) • COREL image database • QS2: 1000 images from 10 selected categories • Each of 10 categories contains 100 images and is used as queries. Table 1. Image Categories in Query Set 2 Robotics Lab, CSIE NCKU

  14. 60.00% Attention-Based System Global 55.00% 50.00% 45.00% Precision 40.00% 35.00% 30.00% 25.00% 20.00% 10 20 30 40 50 60 70 80 90 100 Scope Experiment Result (2) Robotics Lab, CSIE NCKU

  15. 80.00% Attention-Based System Global 70.00% 60.00% 50.00% Precision 40.00% 30.00% 20.00% 10.00% 0.00% 1 2 3 4 5 6 7 8 9 10 Category ID Experiment Result (3) Robotics Lab, CSIE NCKU

  16. Query Image Experimental Results (4) First-time retrieval results Precision = 5/10 Precision = 7/20 Robotics Lab, CSIE NCKU

  17. Experimental Results (5) First-time feedback results Precision = 8/10 Precision = 17/20 Robotics Lab, CSIE NCKU

  18. Experimental Results (6) Second-time feedback results Precision = 10/10 Precision = 20/20 Robotics Lab, CSIE NCKU

  19. Conclusion • The major work in this study is integrating attention-based image retrieval with the relevance feedback algorithm using multiple positive and negative groups. • The system guides the user in clustering positive feedbacks by providing heuristic pre-clustering results. Then the user can revise the clusters manually. Robotics Lab, CSIE NCKU

  20. Experiment Result - Video Demo Robotics Lab, CSIE NCKU

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