Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval

• URL: http://arxiv.org/abs/2304.11734v1
• Date: Sun, 23 Apr 2023 19:51:25 GMT
• Title: Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval
• Authors: Mehdi Rafiei and Alexandros Iosifidis
• Abstract summary: We propose a regularized loss for Variational Auto-Encoders (VAEs) forcing the model to focus on a given class of interest. As a result, the model learns to discriminate the data belonging to the class of interest from any other possibility. Experimental results show that the proposed method outperforms its competition in both in-domain and out-of-domain retrieval problems.
• Score: 95.42181254494287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Using a discriminative representation obtained by supervised deep learning methods showed promising results on diverse Content-Based Image Retrieval (CBIR) problems. However, existing methods exploiting labels during training try to discriminate all available classes, which is not ideal in cases where the retrieval problem focuses on a class of interest. In this paper, we propose a regularized loss for Variational Auto-Encoders (VAEs) forcing the model to focus on a given class of interest. As a result, the model learns to discriminate the data belonging to the class of interest from any other possibility, making the learnt latent space of the VAE suitable for class-specific retrieval tasks. The proposed Class-Specific Variational Auto-Encoder (CS-VAE) is evaluated on three public and one custom datasets, and its performance is compared with that of three related VAE-based methods. Experimental results show that the proposed method outperforms its competition in both in-domain and out-of-domain retrieval problems.

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