Large scale image retrieval based on adaptive Dense-SIFT

In this paper, firstly, an adaptive Dense-SIFT feature extraction method is proposed, which can adaptively adjust the size of local window using the edge information of image. Next, a large scale image retrieval method is proposed. The adaptive Dense-SIFT features are extracted from the database images. Bag of Word (BoW) model is then adopted to create the corresponding histograms of visual words frequency to represent the features. To efficiently describe the image content, the feature vectors are constructed by combining the visual words histograms of Dense-SIFT feature with the 72-dimensional HSV (Hue, Saturation, Value) color feature. In retrieval process, the top-h most similar images are returned by computing the similarity between the feature vectors of querying image and those of the images in database. Finally, to further improve the accuracy, the returned images are re-ranked with context similarity information. The experimental results on Corel-5K and Oxford Buildings dataset show that the proposed method outperforms the existing image retrieval methods.