Image classification based on support vector machine and the fusion of complementary features
This work addresses image classification accuracy for pattern recognition applications, but it is incremental as it builds on existing BOW and SVM techniques.
The paper tackled low classification accuracy in image classification by combining three complementary features (PHOW, PHOC, PHOG) with an improved BOW model and SVM-based fusion, achieving a 7%-17% accuracy improvement over traditional BOW methods on the Caltech 101 database.
Image Classification based on BOW (Bag-of-words) has broad application prospect in pattern recognition field but the shortcomings are existed because of single feature and low classification accuracy. To this end we combine three ingredients: (i) Three features with functions of mutual complementation are adopted to describe the images, including PHOW (Pyramid Histogram of Words), PHOC (Pyramid Histogram of Color) and PHOG (Pyramid Histogram of Orientated Gradients). (ii) The improvement of traditional BOW model is presented by using dense sample and an improved K-means clustering method for constructing the visual dictionary. (iii) An adaptive feature-weight adjusted image categorization algorithm based on the SVM and the fusion of multiple features is adopted. Experiments carried out on Caltech 101 database confirm the validity of the proposed approach. From the experimental results can be seen that the classification accuracy rate of the proposed method is improved by 7%-17% higher than that of the traditional BOW methods. This algorithm makes full use of global, local and spatial information and has significant improvements to the classification accuracy.