Learning binary codes for fast image retrieval with sparse discriminant analysis and deep autoencoders

Article type: Research Article

Authors: Hong, Son An^a | Huu, Quynh Nguyen^{b; *} | Viet, Dung Cu^b | Thi Thuy, Quynh Dao^c | Quoc, Tao Ngo^d

Affiliations: [a] Viet-Hung University, HaNoi, Viet Nam | [b] Thuyloi University, HaNoi, Viet Nam | [c] Posts and Telecommunications Institute of Technology, HaNoi, Viet Nam | [d] Institute of Information Technology, Vietnam Academy of Science and Technology, HaNoi, Viet Nam

Correspondence: [*] Corresponding author: Quynh Nguyen Huu, Thuyloi University, HaNoi, Viet Nam. E-mail: [email protected].

Abstract: Image retrieval with relevant feedback on large and high-dimensional image databases is a challenging task. In this paper, we propose an image retrieval method, called BCFIR (Binary Codes for Fast Image Retrieval). BCFIR utilizes sparse discriminant analysis to select the most important original feature set, and solve the small class problem in the relevance feedback. Besides, to increase the retrieval performance on large-scale image databases, in addition to BCFIR mapping real-valued features to short binary codes, it also applies a bagging learning strategy to improve the ability general capabilities of autoencoders. In addition, our proposed method also takes advantage of both labeled and unlabeled samples to improve the retrieval precision. The experimental results on three databases demonstrate that the proposed method obtains competitive precision compared with other state-of-the-art image retrieval methods.

Keywords: Content-based image retrieval (CBIR), sparse discriminant analysis, deep autoencoder, binary code

DOI: 10.3233/IDA-226687

Journal: Intelligent Data Analysis, vol. 27, no. 3, pp. 809-831, 2023