Discrete aggregation hashing for image set classification

Abstract

With the development of vision technology, image set classification (ISC) has flourished in the image processing field. Different from the one-shot image classification, ISC focuses on the set rather than a one-shot image. Hence, ISC can synthesize the abundant set information to alleviate various appearance variations. Despite the great success of the existing ISC methods, there are still some problems: (1) They usually face an expensive time complexity, which directly limits the practical application; (2) They largely ignore the intrinsic relationships between different sets. In light of this, we propose a novel Discrete Aggregation Hashing (DAH) for fast ISC. To be specific, to extract more semantic information from each set and each sample, we adopt the same projection standard to embed dual semantic labels (i.e., sample label and set label) into instance and set hash codes. Then we regard set hash codes as set-specific centers. A hashing aggregation strategy is proposed to learn compact discriminative instance hash codes via iteratively aggregating intrinsic neighborhood representations around each central node. Therefore, instance hash codes can obtain greater intra-set compactness and inter-set separability. Extensive experiments demonstrate that our DAH can obtain promising performance and outperform these state-of-the-art ISC methods on four image set datasets.