Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval
This work addresses the challenge of accurately ranking similarity for multi-label images in retrieval systems, offering an incremental improvement over existing deep hashing methods.
The paper tackled the problem of multi-label image retrieval by redefining pairwise similarity from hard assignments to instance-based percentages, resulting in a new deep hashing method that outperforms competing methods and achieves state-of-the-art performance on three datasets.
Hash coding has been widely used in the approximate nearest neighbor search for large-scale image retrieval. Recently, many deep hashing methods have been proposed and shown largely improved performance over traditional feature-learning-based methods. Most of these methods examine the pairwise similarity on the semantic-level labels, where the pairwise similarity is generally defined in a hard-assignment way. That is, the pairwise similarity is '1' if they share no less than one class label and '0' if they do not share any. However, such similarity definition cannot reflect the similarity ranking for pairwise images that hold multiple labels. In this paper, a new deep hashing method is proposed for multi-label image retrieval by re-defining the pairwise similarity into an instance similarity, where the instance similarity is quantified into a percentage based on the normalized semantic labels. Based on the instance similarity, a weighted cross-entropy loss and a minimum mean square error loss are tailored for loss-function construction, and are efficiently used for simultaneous feature learning and hash coding. Experiments on three popular datasets demonstrate that, the proposed method outperforms the competing methods and achieves the state-of-the-art performance in multi-label image retrieval.