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Efficient Image Retrieval Based on Regions of Interest for Enhanced Searching

This study explores a new image retrieval approach focusing on regions of interest to improve search efficiency. The retrieval procedure involves image signatures, clustering, and similarity models for accurate results. Experimental studies evaluate the performance under various query types, emphasizing the importance of ROI queries in large datasets. The conclusions highlight the significance of fast retrieval and handling different image sizes effectively.

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Efficient Image Retrieval Based on Regions of Interest for Enhanced Searching

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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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