Class-Specific Variational Auto-Encoder for Content-Based Image Retrieval
This addresses a domain-specific retrieval problem for image search applications, offering an incremental improvement over existing VAE methods.
The paper tackles the problem of content-based image retrieval focusing on a specific class of interest by proposing a regularized loss for Variational Auto-Encoders, resulting in outperforming three related VAE-based methods on three public and one custom dataset for both in-domain and out-of-domain retrieval.
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.