Supervised Deep Hashing for Hierarchical Labeled Data
This work addresses the need for more efficient and accurate image retrieval systems by leveraging hierarchical label information, representing an incremental improvement over prior hashing techniques.
The paper tackles the problem of large-scale image retrieval by proposing a supervised deep hashing method that incorporates hierarchical label relations, which are often ignored in existing methods, and it outperforms state-of-the-art baselines on real-world datasets.
Recently, hashing methods have been widely used in large-scale image retrieval. However, most existing hashing methods did not consider the hierarchical relation of labels, which means that they ignored the rich information stored in the hierarchy. Moreover, most of previous works treat each bit in a hash code equally, which does not meet the scenario of hierarchical labeled data. In this paper, we propose a novel deep hashing method, called supervised hierarchical deep hashing (SHDH), to perform hash code learning for hierarchical labeled data. Specifically, we define a novel similarity formula for hierarchical labeled data by weighting each layer, and design a deep convolutional neural network to obtain a hash code for each data point. Extensive experiments on several real-world public datasets show that the proposed method outperforms the state-of-the-art baselines in the image retrieval task.