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A simultaneous feature adaptation and feature selection method for content-based image retrieval systems
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DOI：10.1016/j.knosys.2012.10.011.png)
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In content-based image retrieval (CBIR) applications, each database needs its corresponding parameter setting for feature extraction. However, most of the CBIR systems perform indexing by a set of fixed and pre-specific parameters. On the other hand, feature selection methods have currently gained considerable popularity to reduce semantic gap. In this regard, this paper is devoted to present a hybrid approach to reduce the semantic gap between low level visual features and high level semantics, through simultaneous feature adaptation and feature selection. In the proposed approach, a hybrid meta-heuristic swarm intelligence-based search technique, called mixed gravitational search algorithm (MGSA), is employed. Some feature extraction parameters (i.e. the parameters of a 6-tap parameterized orthogonal mother wavelet in texture features and quantization levels in color histogram) are optimized to reach a maximum precision of the CBIR systems. Meanwhile, feature subset selection is done for the same purpose. A comparative experimental study with the conventional CBIR system is reported on a database of 1000 images. The obtained results confirm the effectiveness of the proposed adaptive indexing method in the field of CBIR. (c) 2012 Elsevier B.V. All rights reserved.
Keyword:
Image retrieval
Adaptive feature extraction
Feature selection
Parameterized wavelet
Gravitational search algorithm
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A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video.png)
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