Optimizing Image Retrieval: A Two-Step Content-Based Image Retrieval System Using Bag of Visual Words and Color Coherence Vectors

Content-Based Image Retrieval (CBIR) systems play a crucial role in efficiently managing and retrieving images from large datasets based on visual content. This paper presents a novel bi-layer CBIR system that integrates Bag of Visual Words (BoVW) and Color Coherence Vector (CCV) methods to enhance image retrieval accuracy and performance by leveraging the strengths of both feature extraction techniques. In the first layer, the BoVW approach extracts local features and represents images as histograms of visual word occurrences, facilitating efficient initial filtering. In the second layer, CCV features are extracted from the top retrieved images to capture the spatial coherence of colour regions, providing a detailed colour signature. By combining the merits of both layers, the proposed system achieves higher retrieval precision and recall compared to the traditional single-layer approaches. Experimental results demonstrate the effectiveness of the bi-layer CBIR system in retrieving relevant images with improved accuracy, making it a valuable tool for application in image databases, digital libraries, and multimedia content management.