Stacked Siamese Neural Network (SSiNN) on Neural Codes for Content-based Image Retrieval

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 repos...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Muni Kumar, Gopu V R, Madhavi, D
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Best practice
CBIR
Content-based Image Retrieval
Content-based retrieval
Data models
Datasets
Deep learning
Image retrieval
Machine learning
Measurement
Neural networks
Neurons
Queries
Semantic gap
Semantic search
Siamese Network
Task analysis
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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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3298216