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Study of image retrieval and classification based on adaptive features using genetic algorithm feature selection
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AI
DOI：10.1016/j.eswa.2014.04.033.png)
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Abstract
En 中文
This paper proposes a genetic algorithm feature selection (GAFS) for image retrieval systems and image classification. Two texture features of adaptive motifs co-occurrence matrix (AMCOM) and gradient histogram for adaptive motifs (GHAM) and color feature of an adaptive color histogram for K-means (ACH) were used in this paper. In this paper, the feature selections have adopted sequential forward selection (SFS), sequential backward selection (SBS), and genetic algorithms feature selection (GAFS). Image retrieval and classification performance mainly build from three features: ACH, AMCOM and GHAM, where the classification system is used for two-class SVM classification. In the experimental results, we can find that all the methods regarding feature extraction mentioned in this study can contribute to better results with regard to image retrieval and image classification. The GAFS can provide a more robust solution at the expense of increased computational effort. By applying GAFS to image retrieval systems, not only could the number of features be effectively reduced, but higher image retrieval accuracy is elicited. (C) 2014 Elsevier Ltd. All rights reserved.
Keywords:
Color feature
Texture features
Genetic algorithms
Feature selection
Support vector machine
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W