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In the popularly used BoVW and VLAD models for image retrieval, feature extraction is easily affected by the color and textures of images. Also, the clustering results of K-means algorithm used in these two models is usually affected by the initial cluster centroids. In order to solve these problems, a generic object regions matching based VLAD model for image retrieval is proposed. In this model, multi-threshold for image segmentation is proposed for the extraction of SURF features. Then the location information of SURF features are utilized for the Gaussian Mixture Model clustering instead of K-means algorithm. Finally, VLAD descriptors are calculated according to the features in each cluster and used for similar image searching. Our proposed Multi-Threshold Segmentation and SURF Feature Location based Clustering algorithm can improve the matching accuracy of features and obtain a better codebook such that the feature distribution can be better expressed. Experimental results on the Holidays dataset show that the mAP of our algorithm is higher than the 5 mainstream image retrieval algorithms, which efficiently improves the image retrieval accuracy.

Generic Object Regions Matching Based VLAD Model for Image Retrieval