Uniting Keypoints: Local Visual Information Fusion for Large-Scale Image Search

Abstract

In this paper, we propose a novel approach to address the problem of the huge amount of local features for a large-scale database. First, in each image the local features are organized into dozens of groups by performing the standard $k$ -means clustering algorithm on their spatial positions. Second, a compact descriptor is generated to describe the visual information of each group of local features. Since, in each image, thousands of local features are reorganized into only dozens of groups and each group is described by a single descriptor, the total amount of descriptors in a large-scale database will be greatly reduced. Therefore, we can reduce the complexity of the searching procedure significantly. Further, the generated group descriptors are encoded into binary format to achieve the storage and computation efficiency. The experiments on two benchmark datasets, i.e., UKBench and Holidays, with the Flickr1M distractor database demonstrate the effectiveness of the proposed approach.