Multiple feature fusion based image classification using a non-biased multi-scale kernel machine

Image target classification is an important branch of pattern recognition, especially the multi-class image classification is also a research focus for image recognition and retrieval. Aiming at the image characteristics of WANG dataset, a sub-dataset of Corel dataset, four effective feature extraction methods are studied in this paper, which are the color moment feature, the color distribution feature, the Fourier transform feature and the fractal dimension feature. On this basis, a non-biased multi-scale kernel least squares support vector classifier (LSSVC) is presented. Utilizing the non-biased LSSVC model, we can test and gain the optimal classification correct rate of each single feature, which can be used to determine the weight coefficients of multiple kernel learning. Ultimately, the different kinds of features and the multi-scale classifier can be organically fused. The multi-class image classification experiments show that the method has good generalizability, and can gain better classification precision.