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Image Retrieval Based on Regions of Interest

Image Retrieval Based on Regions of Interest. Source : KNOWLEDGE AND DATA ENGINEERING, IEEE Vol.: 15, No. 4, JULY,2003 pp.1045 - 1049 Author : Khanh Vu, Kien A. Hua, Senior Member, Wallapak Tavanapong Reporter : Shing-Shoung Wang Date : 2005/5/3.

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Image Retrieval Based on Regions of Interest

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  1. Image Retrieval Based on Regions of Interest Source : KNOWLEDGE AND DATA ENGINEERING, IEEE Vol.: 15, No. 4, JULY,2003 pp.1045 - 1049 Author : Khanh Vu, Kien A. Hua, Senior Member, Wallapak Tavanapong Reporter : Shing-Shoung Wang Date : 2005/5/3

  2. Outline • Introduction • Retrieval Procedure • Experimental Study • Conclusions

  3. Introduction • QBE(Query-By-Example) is the most widely supported method. • Existing CBIR(Content-Based Image Retrieval) systems for ROI(region-of-interest). 1.not effective. 2.color histograms disadvantages.

  4. Retrieval Procedure Q Image Signature A Similarity Model for ROI Queries Clustering& Indexing Image Signature

  5. Retrieval Procedure(Cont.) Sample block 16×16 pixels Sampling rate 256 pixels 256 pixels

  6. Retrieval Procedure(Cont.) Handling the Scaling of the Matching Objects. database images query images

  7. Retrieval Procedure(Cont.) • Image Signatures: Apply 7 pairs of mean-variance vector as Image Signatures. ((μ1, σ12),(μ2, σ22),(μ3, σ32),(μ4, σ42),(μ5, σ52),(μ6, σ62),(μ7, σ72)) Core Area upper 1 2 lower 3 4

  8. Retrieval Procedure(Cont.) • Clustering and Indexing: Images signatures are enormous for large data sets. 1.map the signatures of each image into signature points, and cluster them into mininal bounding retangles(MBR). 2.R*-tree.

  9. Retrieval Procedure(Cont.) A Similarity Model for ROI Queries • SamMatch Environment. Munsell color system. • Similarity Measure Wi a weight factor Wi = q ⋅|c/2 - ci| the distance between the color of block i of subimage Q&S

  10. Retrieval Procedure(Cont.) • Ranking Retrieved Images: 1. determine ROI in the image, those that fall within the boundary of S; 2. extract these blocks from the 113 blocks of the image—they constitute the feature vector of the subimage S to be compared; and 3. perform block-to-block comparison to determine the similarity of Q and S according to (1).

  11. Experimental Study • Comparative Studies • Metric. Let A1, A2, . . ., Aq denote the q relevant images in response to a query Q. The recall R is defined for a scope S, S > 0, as:

  12. Experimental Study(Cont.) • 3 Types of NFQs(Noise Free Querys) • Type 1: The query image has the same size as those in the database. The queried object covers only a small region of the query image. • Type 2: The query image has the same size as those in the database. The query is relatively large, covering almost the entire query image. • Type 3: The query image is smaller or larger than the size of the database images.

  13. Experimental Study(Cont.) • Performance Issues Specific to SamMatch R/S averages under specific types of NFQs. (a) Under type-1 NFQs, (b) under type-2 NFQs, and (c) under type-3 NFQs. Corr.:Correlogram SM:SamMatch LCH:Local Color Histogram

  14. Experimental Study(Cont.) • Performance Issues Specific to SamMatch

  15. Conclusions • When retrievaling in large image data sets. 1.ROI queries. 2.Fast retrieval. 3.Different sizes.

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