Source: Pattern Recognition Vol. 38, May, 2005, pp

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Presentation transcript:

Content-based image retrieval using growing hierarchical self-organizing quadtree map Source: Pattern Recognition Vol. 38, May, 2005, pp. 707-722 Author: Sitao Wu, M. K. M. Rahman, Tommy W. S. Chow Reporter: Yung-Chen Chou Date: Mar. 10, 2005

Outline Introduction JSEG Region Segmentation Self-Organizing Maps (SOM) Growing Hierarchical SOM (GHSOM) Proposed Method Experimental Results Conclusions Yung-Chen Chou

Introduction Image index database Feature extraction Feature Similarity matching Query image Basic concept of image retrieval Yung-Chen Chou

Introduction CBIR types: Region segmentation (JSEG) SOM and GHSOM Histogram extraction Color layout extraction Region-based extraction Region segmentation (JSEG) SOM and GHSOM Feedback adjustment Yung-Chen Chou

Introduction Architecture of GHSOQM-based CBIR system Yung-Chen Chou

JSEG region segmentation (Deng et al. 1999) Original image J-image Region-based image Schematic of the JSEG algorithm Yung-Chen Chou

Self-Organizing Maps (Kohonen 1980s) Yung-Chen Chou

Self-Organizing Maps (Kohonen 1980s) Learning rate Yung-Chen Chou

Growing Hierarchical Self-Organizing Maps (Rauber 2002) Yung-Chen Chou

Proposed method Color standard deviation Texture standard deviation ={f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11, f12, f13} JSEG Lab color texture The region Percentage of an image Image x (Lab color space) f1 f2 … f13 : Feature matrix for image x Yung-Chen Chou

Neighborhood function Proposed method Neighborhood function Learning rate Yung-Chen Chou

Proposed method Query image Image x Yung-Chen Chou

Proposed method Y Z Q Y’ Z’ Yung-Chen Chou

Experimental results 1000 images and classify to ten classes The sizes of the images are 384X256, or 256X384 τ = 20; λ=20; Time comparison: GHSOQM SIMPLIcity Training (sec) 1890 No need Query time (sec) 3.08 7.25 Yung-Chen Chou

Experimental results The concept diagram of SIMPLIcity Yung-Chen Chou

Experimental results Yung-Chen Chou

Experimental results Yung-Chen Chou

Conclusions Using a neural network to organize images into a hierarchical structure The query time is largely reduced Do not need retraining all images when adding the new images No discussion about memory use Yung-Chen Chou