Content-based image retrieval by using tree-structured features and multi-layer self-organizing map

Authors:
S. Chow;M. Rahman;Sitao Wu
Affiliations:
Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, People’s Republic of China;Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, People’s Republic of China;Department of Electronic Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, People’s Republic of China
Venue:
Pattern Analysis & Applications
Year:
2006

Citing 0
Cited 10

A flexible multi-layer self-organizing map for generic processing of tree-structured data

Pattern Recognition
Semantic content ranking through collaborative and content clustering

Neurocomputing
Computational accounting in determining Chart of Accounts using nominal data analysis and concept of entropy

Expert Systems with Applications: An International Journal
A hybrid probabilistic framework for content-based image retrieval with feature weighting

Pattern Recognition
A new document representation using term frequency and vectorized graph connectionists with application to document retrieval

Expert Systems with Applications: An International Journal
Multilayer SOM with tree-structured data for efficient document retrieval and plagiarism detection

IEEE Transactions on Neural Networks
Content-based hierarchical document organization using multi-layer hybrid network and tree-structured features

Expert Systems with Applications: An International Journal
A new segmentation approach in structured self-organizing maps for image retrieval

IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
On dealing with imprecise information in a content based image retrieval system

IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Soft label based Linear Discriminant Analysis for image recognition and retrieval

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new approach for content-based image retrieval (CBIR) is described. In this study, a tree-structured image representation together with a multi-layer self-organizing map (MLSOM) is proposed for efficient image retrieval. In the proposed tree-structured image representation, a root node contains the global features, while child nodes contain the local region-based features. This approach hierarchically integrates more information of image contents to achieve better retrieval accuracy compared with global and region features individually. MLSOM in the proposed method provides effective compression and organization of tree-structured image data. This enables the retrieval system to operate at a much faster rate than that of directly comparing query images with all images in databases. The proposed method also adopts a relevance feedback scheme to improve the retrieval accuracy by a respectable level. Our obtained results indicate that the proposed image retrieval system is robust against different types of image alterations. Comparative results corroborate that the proposed CBIR system is promising in terms of accuracy, speed and robustness.