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Color-Texture Analysis for Content-Based Image Retrieval

Explore color and texture features for efficient image retrieval, including segmentation and evaluation methods. Relevant for Intelligent Systems applications.

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Color-Texture Analysis for Content-Based Image Retrieval

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  1. Color-Texture Analysis for Content-Based Image Retrieval Anh-Minh Hoang (W03213684) Supervisor: Vassilis Kodogiannis M.Sc. in Intelligent and Multi-Agent Systems, Harrow School of Computer Science.

  2. Outline • Introduction to the problem • The goals of the work • Introduction to the approach • The relevance of the work to the areas of Intelligent Systems • Related works • Evaluation methods

  3. Introduction • The volume of digital image archives is growing rapidly and has become very large • Large amount of visual data is available on digital libraries or on the WWW. • The needs for searching visual information such as images, videos are emerging

  4. Introduction (cont.) • Manual image annotations can be used to a certain extent to help image search, but the feasibility of such approach to large databases is a questionable issue • Content-based image retrieval (CBIR) aims at efficient retrieval of relevant images from large image databases based on automatically derived imagery features such as color, texture, shape…

  5. Retrieved Images Query Image Query Blobs Image Database Building Index Similarity Assessment Feature Space Introduction (cont.)

  6. Goals • To automatically derive color and texture feature from image • To automatically partition an image into disjoint region coherently different in color and texture (image segmentation) • To build an image retrieval system using color and texture information

  7. Approach • Color-texture measurement (see Minh A. Hoang et al, Signal Processing, pp. 265–275, February 2005) • Multiscale Region-Boundary Refinement for Color-Texture Segmentation • Features and regions indexing and matching for image retrieval

  8. Gaussian color model 1 0.8 0.6 0.4 Gabor filters Color-Texture Feature Input color image 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 Color-texture Feature

  9. Input Image Coarsest Scale Region Initialization Boundary Initialization Seed Placement Region Growing Boundary Specification Region Specification Region Receding Reduce Scale Update Region Information No Update Boundary Information No Seed Placement Yes Yes Output Image Finer Scale Region Growing Segmentation: Multiscale Approach

  10. C1 C4 Color-texture Segmentation Ground truth

  11. #134052 #66075 Color-texture Segmentation (cont.)

  12. Image Retrieval System

  13. Applications in some areas ofIntelligent Systems • Robot vision, object recognition, object tracking (e.g. robot soccer, intelligent vehicles driver assistance…): visual feature extraction and image segmentation is fundamental • Search engines for visual information, automatic annotation of visual database, automatic detection of salient features

  14. Related Works • IBM QBIC, MIT Photobook, Columbia VisualSEEK and WebSEEK, PicToSeek, BlobWord: image retrieval systems • J. Malik et al, “Contour and texture analysis for image segmentation”, International Journal of Computer Vision 43(1), pp. 7–27, 2001 • J. Freixenet et al, “Color Texture Segmentation by Region-Boundary Cooperation”, in The Eighth European Conference on Computer Vision, pp. 250–261, Springer Verlag, (Prague, Czech Republic), may 2004.

  15. Related Works (cont.) • M. Tabb et al, “Multiscale image segmentation by integrated edge and region detection”, IEEE Trans. on Image Processing 6(5), pp. 642–655, 1997 • P. Schroeter et al, “Hierarchical image segmentation by multi-dimensional clustering and orientation-adaptive boundary refinement”, Pattern Recognition28(5), pp. 695–709, 1995. • M. Mirmehdi and M. Petrou, “Segmentation of color textures”, IEEE Trans. on PAMI 22(2), pp. 142–159, 2000. • A. W. M. Smeulders et al, “Content-based image retrieval at the end of the early years”, IEEE Trans. on PAMI22(12), pp. 1349–1380, 2000.

  16. Evaluation methods • Evaluation of color-texture feature extraction and image segmentation based on: • Compare with ground truth samples (or with human segmentations) • Compare to results from other works • Verify by human perception (heuristics)

  17. Evaluation methods (cont.) • Evaluation of image retrieval system based on: • Average precision vs. number of retrieved images for several query types • Average number of steps to get to desired results based on relevant feedbacks • Heuristics (verify by human perception)

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