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Image Classification based on BOW (Bag-of-words) has broad applicationprospect in pattern recognition field but the shortcomings are existed becauseof single feature and low classification accuracy. To this end we combine threeingredients: (i) Three features with functions of mutual complementation areadopted to describe the images, including PHOW (Pyramid Histogram of Words),PHOC (Pyramid Histogram of Color) and PHOG (Pyramid Histogram of OrientatedGradients). (ii) The improvement of traditional BOW model is presented by usingdense sample and an improved K-means clustering method for constructing thevisual dictionary. (iii) An adaptive feature-weight adjusted imagecategorization algorithm based on the SVM and the fusion of multiple featuresis adopted. Experiments carried out on Caltech 101 database confirm thevalidity of the proposed approach. From the experimental results can be seenthat the classification accuracy rate of the proposed method is improved by7%-17% higher than that of the traditional BOW methods. This algorithm makesfull use of global, local and spatial information and has significantimprovements to the classification accuracy.

Image classification based on support vector machine and the fusion of complementary features