Deep Neural Networks Based Feature Extraction with CBIR: An Exhaustive Analysis

The need for having sufficient space to store and retrieve data has grown in importance in today's fast-paced worlde. Hence, end-users struggle to access particular facts, leading to difficulties in obtaining reliable statistics. The text-based image retrieval (TBIR) system is being enhanced by researchers to address these issues. Based on the user's query or annotation, TBIR finds relevant facts. This is the best method for users to get all the important data. The database unfortunately produces more unwanted data due to the complexity and length of the operation. A technology known as content-based image retrieval (CBIR), offers a way around this problem. The user's query or input images are used to determine the relevant output images. Machine learning and deep learning algorithms are highly suited for CBIR- based image classification and prediction. The notion of machine learning as a potential remedy for the semantic gap problem has been extensively studied. Given the recent successes of deep learning, there is evidence to suggest that we are capable of bridging the semantic gap in CBIR. The various techniques for feature extraction and similarity measurement are thoroughly reviewed in this paper. The benefits and drawbacks of deep learning networks and CBIR-based machine learning techniques are further investigated through comparison analysis portion. Consequently, detailed explanations of techniques utilizing deep learning for identifying skin disorders are provided. Following to that, a variety of CBIR approaches and the Corel 5K dataset are used to assess the various performance measures.