In the traditional bag of word model, the SIFT features are extracted from the gray space of image, which cannot reflect the color information of image. To solve this problem, we propose to use a visual vocabulary tree vector that fuses the color feature to represent image contents. SIFT features are extracted and the vocabulary tree is built to obtain the SIFT features of images.The Kmeans method is used to cluster the HSV values of all images in the image library so as to obtain the representation vector of the color word bag based on the HSV space, there by avoiding the quantization error brought by the traditional color histogram method.The fusion of SIFT features and color word bag features completes the fusion of global and local features of the image.
Finally, by calculating the similarities of the fusion features and sorting them from high to low,the image retrieval is completed. In order to validate the effectiveness of the proposed method, we choose Corel image database to analyze the performance of the algorithm, evaluate it from subjective evaluation and objective evaluation criteria, and compare it with the traditional method. The results show that,compared with the single feature method, the proposal improves the retrieval performance of feature fusion. The average retrieval precision and the recall ratio of the feature fusion method are all improved to some extent.