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A Hybrid Region Weighting Approach for Relevance Feedback in Region-Based Image Search on the Web

A Hybrid Region Weighting Approach for Relevance Feedback in Region-Based Image Search on the Web. Deok-Hwan Kim, Jae-Won Song, Ju-Hong Lee INHA University. Contents. Problem of CBIR Proposed RBIR Hybrid Region Weighting Experiment and Results Conclusion. Content Based Image Retrieval.

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A Hybrid Region Weighting Approach for Relevance Feedback in Region-Based Image Search on the Web

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  1. A Hybrid Region Weighting Approach for Relevance Feedback in Region-Based Image Search on the Web Deok-Hwan Kim, Jae-Won Song, Ju-Hong Lee INHA University

  2. Contents • Problem of CBIR • Proposed RBIR • Hybrid Region Weighting • Experiment and Results • Conclusion

  3. Content Based Image Retrieval • CBIR • utilizes unique features (shape, color, texture) of images Users prefer • To retrieve relevant image by semantic categories • But, CBIR can not capture high-level semantics in user’s mind

  4. Problem of CBIR (1) • Problem • Focused on developing effective global features  Can not capture properties of an object The gap between low-level feature and high-level semantics System Semantic Gap Query Image User

  5. Problem of CBIR(2) • Solutions • Relevance Feedback (RF) • Region-based Image Retrieval (RBIR)

  6. Relevance Feedback • Relevance Feedback • Learns the associations between high-level semantics and low-level features • Relevance Feedback Phase • User identifies relevant images within the returned set • System utilizes user feedback in the next round • To modify the query (to retrieve better results) • This process repeats  until user is satisfied

  7. Problem of CBIR(2) • Solutions • Relevance Feedback (RF) • Region-based Image Retrieval (RBIR)

  8. RBIR(Region-Based Image Retrieval) • Region-Based approaches • Represent image at the object level • The main objective • Enhance the ability of capturing user’s perception • More meaningful retrieval • Image similarity measures • EMD • Weighting of region • key factor of similarity definition.

  9. Image Top k Retrieved Set Segmentation User Feedback Loop Image DB Region 1 Region n Relevant Set Feature extraction Feature extraction Adaptive Clustering Weight Computation Weight Computation Q=(q,d,w, k) ClusterRepresentatives Region basedCluster Set Q =(q,d,w, k) EMD match Proposed RBIR Approach

  10. Adaptive Region Clustering(1) • Merges similar regions in the relevant set  reduce retrieval speed • T2 > Threshold : separate two clusters • T2 <=Threshold : merge two clusters

  11. (g-1)th level C1 Cg-1 C1 Ck Cg gth level C1 C2 Cm Cn Region Image Adaptive Region Clustering(2)

  12. Image Segmentation and Region Representation • Normalized cut segmentation • Discriminate foreground object regions and background regions • Region Representation in an Image • Twelve dimensional color and shape features • Color feature • mean, standard deviation of color in L*a*b color space • Shape feature • Compactness and convexity, region size, region location, and variance of region pixels from the region center of mass

  13. Region Weighting • Existing Region Weighting • Area Percentage • RF (Region Frequency) * IIF (Inverse Image Frequency) • Suggested Region Weighting • spatial locations of regions • region size in an image

  14. S xn x1 RB x3 RA x2 Center C random points l RC Hybrid Region Weighting • Assume that more important region • appear in center area of an image • tend to occupy larger area • To consider image’s Spatial location

  15. Hybrid Region Weighting • Region Importance • Calculated by summarizing the reciprocal function values with respect to all pixel locations x of region Rki • However, it is difficult • Instead, use the asymptotic distance function by applying the Monte-Carlo method

  16. Hybrid Region Weighting • Region Weight • Decay Factor β (0≤ β ≤1) • reduce the effect of previous relevant image • We assume that there are n relevant images I1…In • prior images : I1…Im • new images : Im+1… In

  17. Hybrid Region Weighting • New region weights using decay factor is as follows:

  18. Three weights for regions of an animal image area percentage 0.16 region frequency 0.10 area & location 0.06 area percentage 0.01 region frequency 0.11 area & location 0.07 area percentage 0.06 region frequency 0.12 area & location 0.12 area percentage 0.24 region frequency 0.04 area & location 0.42 area percentage 0.06 region frequency 0.23 area & location 0.18 area percentage 0.47 region frequency 0.39 area & location 0.15

  19. Experiment and Results(1) • k-NN query • used to accomplish the similarity-based match • k = 100. • For RBIR with RF approach • Use adaptive region clustering method • 10,000 general purpose color images from COREL • 40 random initial query • five feedback • For decay factor, empirically, β =0.3 • To evaluate the performance, we compare • Area percentage, Region frequency, Area & location

  20. Experiment and Results(2) Performance evaluation

  21. Conclusion • The main contribution • Calculate the importance of regions by using the hybrid weighting method • Cumulate it based on user’s feedback information to better represent semantic importance of a region in a given query • Proposed weighting method can also be incorporated into any RBIR system on Web • It put more emphasis on the latest relevant images that express the user’s query concept more precisely • Experimental results • Show the superiority of the proposed method over other weighting methods in terms of efficiency and effectiveness

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