Deep Learning Model for Retrieving Color Logo Images in Content Based Image Retrieval

Abstract

Content-Based Image retrieval (CBIR) has gained a magnificent deal of consideration because of the digital image data's epidemic growth. The advancement of deep learning has enabled Convolutional Neural Networks to become an influential technique for extraction of discriminative image features. In recent years, convolutional neural networks (CNNs) have proven extremely effective at extracting unique information from images. In contrast to text-based image retrieval, CBIR gathers comparable images based primarily on their visual content. The use of deep learning, especially CNNs, for feature extraction and image processing has been shown to perform better than other techniques. In the proposed study, we investigate CNNs for CBIR focusing on how well they extract discriminative visual features and facilitate accurate image retrieval.  Also Principal Component Analysis and Linear Discriminant Analysis are combined for optimization of features resulting in boosting the retrieval results.  Using hierarchical representations learned by CNNs, we aim to improve retrieval accuracy and efficiency. In comparison with conventional retrieval techniques, our proposed CBIR system shows superior performance on a benchmark dataset.