| Summary: | Content-based image retrieval (CBIR) represents a class of problems that aims at finding relevant images in response to an image-based search query. The CBIR systems use similarity measures or distance metrics between a group of representative features in the query image and those in the image repository. Traditionally, these features were generated by hand, employing image features such as colour, texture, shape, and so on. Due to the fact that these methods do not provide a comprehensive perspective of the images, they cannot be widely utilized in contemporary CBIR systems. This is due to the so-called semantic gap between query intent and system perspective. The most recent advancements in deep learning offer a viable alternative to manually built features, leveraging the representational learning capability of deep neural networks. This paper presents a method of implementing a CBIR system using a multi-stage approach known as classify, differentiate, and retrieve (CDR). The first stage involves using a deep neural network to encode the images. Later, a custom-trained stacked Siamese Neural network (SSiNN) is employed to differentiate the latent space representation of the images obtained from the first stage. The experimental results for the CIFAR-10 dataset were presented, along with an algorithm for applying this strategy to any generic dataset. Experimental outcomes demonstrate that the proposed strategy is superior to the current best practices.
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