Abstract:

Content-based image retrieval (CBIR) systems play a crucial role in efficiently managing and retrieving images based on their visual content. Traditional CBIR methods often rely on handcrafted features, limiting their ability to capture and abstract visual information. With the advent of deep learning, particularly convolutional neural networks (CNNs) in enhancing CBIR systems directly from image data.This paper proposes a novel approach for CBIR leveraging deep learning techniques. To evaluate the effectiveness of our method, we conduct experiments on standard image datasets and compare our results with traditional CBIR techniques. We utilize a pretrained CNN architecture, such as ResNet,Mobilenet ,VGG, to extract high-level features from images, which are then used to measure similarities between query images and images within a database.

Description:

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