Retrieving images by multiple samples via fusing deep features

Abstract

Most existing image retrieval systems search similar images on a given single input, while querying based on multiple images is not a trivial. In this paper, we describe a novel image retrieval paradigm that users could input two images as query to search the images that include the content of the two input images-synchronously. In our solution, the deep CNN feature is extracted from each single query image and then fused as the query feature. Due to the role of the two query images is different and changeable, we propose the FWC (Feature weighting by Clustering), a novel algorithm to weight the two query features. All the CNN features in the whole dataset are clustered and the weight of each query is obtained by the distance to the mutual nearest cluster. The effectiveness of our algorithm is evaluated in PASCAL VOC2007 and Microsoft COCO datasets.