摘要：In this article, we propose an image classification algorithm based on Bag of Visual Words model and multi-kernel learning. First of all, we extract the D-SIFT (Dense Scale-invariant Feature Transform) features from images in the training set. And then construct visual vocabulary via K-means clustering. The local features of original images are mapped to vectors of fixed length through visual vocabulary and spatial pyramid model. At last, the final classification results are given by generalized multiple kernel proposed by this paper. The experiments are performed on Caltech-101 image dataset and the results show the accuracy and effectiveness of the algorithm
