A Novel Image Retrieval Approach with Bag-of-Word Model and Gabor Feature

In the past few years, image retrieval has been one of the hot spots in computer vision field. Among many image retrieval techniques, Bag-of, Word (BoW) model is one of the effective and efficient methods that can search images with visual vocabularies and it is insensitive to massive data and various geometric attacks. But the classical BoW algorithm used some descriptors as its visual words, such as SIFT, which are also used to build visual vocabulary. The main problem with traditional BoW algorithm is that the visual vocabulary could not reflect the spatial information of visual words. And most BoW algorithms utilize one single feature as their feature vector, some other important features are ignored. All these factors have influenced the accuracy of final result in traditional BoW model. In order to solve the problem, we propose a novel image retrieval method with BoW and Gabor feature. The paper first proposes a new saliency map extraction method, and then the saliency score is used to build visual vocabulary. At last, Gabor feature is combined with visual vocabulary together to compute similarity. In order to test the effectiveness of the algorithm, we evaluate our method on the SIMPLICITY dataset and Stanford dataset. Experiments results demonstrate the effectiveness and accuracy of the proposed method.