A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm

2011 ◽  
Vol 60 (10) ◽  
pp. 3318-3325 ◽  
Author(s):  
Chih-Chin Lai ◽  
Ying-Chuan Chen
Keyword(s):  
Genetic Algorithm ◽  
Image Retrieval ◽  
Retrieval System ◽  
Interactive Genetic Algorithm ◽  
Image Retrieval System

Research on Constructing a Multi-Label Image Annotation and Retrieval System

2010 ◽  
Vol 20-23 ◽  
pp. 559-564
Author(s):  
Yu Long Tian ◽  
Ran Li ◽  
Jian Jiang Lu ◽  
Ya Fei Zhang ◽  
Zi Ning Lu
Keyword(s):  
Genetic Algorithm ◽  
Image Retrieval ◽  
High Precision ◽  
Retrieval System ◽  
Image Annotation ◽  
Visual Features ◽  
Semantic Level ◽  
Optimal Weights ◽  
Image Retrieval System ◽  
Optimal Feature

In this paper, we design and construct a multi-label image annotation and retrieval system. Various MPEG-7 low level visual features are employed for representing images. For image annotation, a bi-coded genetic algorithm is employed to select optimal feature subsets and corresponding optimal weights for every one vs. one SVM classifiers. After an unlabelled image is segmented into several regions, pre-trained SVMs are used to annotate each region, final label is obtained by merging all the region labels. Based on multi-label of image, image retrieval system provides keyword-based image retrieval service. Multi-labels can provide abundant descriptions for image content in semantic level, high precision annotation algorithm further improve annotation performance.

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