A NOVEL CNN-BASED MATCH KERNEL FOR IMAGE RETRIEVAL

The recent decade has witnessed remarkable developments of SIFT based approaches for image retrieval. However, such approaches arc inherently insufficient in handling the semantic gap and large viewpoint changes, leading to inferior performance. To address these limitations, this paper extends SIFT-based match kernels by integrating the match functions for SIFT and CNN features. Specifically, a thresholded exponential match kernel for CNN features is proposed to calculate their semantic similarity of images. Thus, images having semantic similarity lower than the threshold will be filtered out, and the remaining images will be assigned semantic weights to adjust the similarity scores measured by SIFT. Enhanced discriminability is achieved in the proposed method by effectively encoding the complementary cues from SIFT and CNN features into the match kernels. Extensive evaluations on benchmark datasets demonstrate the effectiveness of our method, which outperforms existing methods by a considerable margin.