Abstract
Hashing technique has been extensively utilized in approximate nearest neighbor (ANN) search for large-scale image retrieval by virtue of its storage simplicity and computational efficiency. Recently, many researches show that hashing methods based. on deep neural networks (DNNs) can improve retrieval accuracy by simultaneously learning both deep feature representation and hashing functions in an end-to-end framework. Most deep supervised hashing methods aim to preserve the distance or similarity between data points using the similarity relationships constructed based on semantic labels of images, while ignoring the classification ability of the generated hash codes. However, the semantic labels themselves carry more information than the corresponding similarity labels. We propose an Improved Pairwise-based Deep Hashing (IPDH) method to generate hash codes with powerful classification ability by exploring the global distribution of semantic labels. Specifically, the proposed IPDH method aims to minimize the information loss generated during the process of classification prediction to ensure that the output predicted labels of the network model has a similar distribution with those from the original semantic labels. Comprehensive experiments show that the proposed IPDH method can obtain better improvement than other state-of-the-art algorithms.
Highlights
With the development of information technology, the massive and high-dimensional multimedia information resources on the web have greatly promoted the development of large-scale visual search [1]–[4]
The contributions of this article are as follows: (1) We propose a novel robust classification metric based on JS divergence to obtain optimal hash codes with high classification capability by optimizing the relationship between the semantic labels of images and the predicted labels learned by Deep Convolutional Neural Networks (DCNNs). (2) We propose an Improved Pairwise-based Deep Hashing (IPDH) method, which can simultaneously learn and optimize both the classification quality and the distance-based similarity in the learning process
Yao et al.: IPDH: An IPDH Method for Large-Scale Image Retrieval TABLE 4. mAP on MS-COCO dataset for different conditions (%)
Summary
With the development of information technology, the massive and high-dimensional multimedia information resources on the web have greatly promoted the development of large-scale visual search [1]–[4]. Traditional Text-Based Image Retrieval (TBIR) [5] generally queries images in the form of keywords, and its development has been relatively mature. Due to the limitation of controlled vocabulary, the TBIR system cannot efficiently deal with ever-changing images. Content-Based Image Retrieval (CBIR) [6], [7] has received extensive attention in people’s lives, which can further explore the semantic content of multimedia data resources. Compared with the traditional linear scanning method, Approximate Nearest Neighbor (ANN) search is usually used to ensure the real-time response speed.
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