1. Color-Texture Analysis for Content-Based Image Retrieval
Anh-Minh Hoang (W )
Supervisor: Vassilis Kodogiannis
M.Sc. in Intelligent and Multi-Agent Systems,
Harrow School of Computer Science.
17 September 2018

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
17 September 2018

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
17 September 2018

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…
17 September 2018

5. Introduction (cont.) Retrieved Images Query Image Image Database
Similarity Assessment
Retrieved Images
Building
Index
Query Image
Query Blobs
Image Database
Feature Space
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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
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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
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8. Color-texture Feature-1
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Input color image
Gaussian color model
Gabor filters
Color-Texture Feature
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9. Segmentation: Multiscale Approach
Input Image
Boundary Initialization
Region Initialization
Update Region Information
Update Boundary Information
Seed Placement
Region Growing
Region Specification
Boundary Specification
Region Receding
Reduce Scale No
Coarsest Scale
Finer Scale
Yes
Output Image
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10. Color-texture Segmentation
Ground truth C1 C4
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11. Color-texture Segmentation (cont.)
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12. Image Retrieval System
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13. Applications in some areas of Intelligent 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
17 September 2018

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.
17 September 2018

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 Recognition 28(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 PAMI 22(12), pp. 1349–1380, 2000.
17 September 2018

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)
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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)
17 September 2018