Research on Novel Image Classification Algorithm based on Multi-Feature Extraction and Modified SVM Classifier
Bingquan Huo, Fengling Yin
Journal of Multimedia Processing and Technologies, Vol. 7, No. 2, 2016.
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Abstract:
In this research paper, we conduct theoretical analysis and numerical analysis on novel image classification algorithm based on multi-feature extraction and modified SVM classifier. Image object classification and detection are two important basic problems in the study of computer vision, image segmentation, object tracking, behavior analysis and so on the basis of other high-level vision tasks. Existing image classification method can make full use of every single feature between the complementary characteristics of the extracted features of a large number of redundant information, which can lead to image classification accuracy is not high. For this, put forward an improved support vector machine (SVM) based on characteristics and integrated method of image classification. This method can extract comprehensive description of image content features, using principal component analysis to extract the characteristics of transformation, remove redundant information. The experimental result proves the effectiveness and feasibility of the proposed algorithm. In the final part, we conclude the paper and set up the prospect for the future research.