Deep Uniqueness-Aware Hashing for Fine-Grained Multi-Label Image Retrieval

Abstract

Deep supervised hashing methods for multi-label image retrieval have achieved great success nowadays. However, these methods only take the similarity between the database images and the query images into account, but they ignore the uniqueness of the database images when deciding on their rankings. Here we present a novel Deep Uniqueness-Aware Hashing (DUAH) method for learning hash functions that preserve not only multilevel semantic similarity between multi-label images, but also the unique semantic structure of each image. In our approach, both the pairwise label information and the classification information are fully exploited to maximize the discriminability of the output binary codes within one stream framework. Extensive evaluations conducted on three widely used multi-label image benchmarks demonstrate that DUAH can support fine-grained multi-label image retrieval better.