Hash based image indexing framework using deep earning

Convolutional neural networks (CNN) have demonstrated their effectiveness in extracting discriminative features from images, allowing for accurate image recognition and classification. Traditional search approaches, on the other hand, become computationally expensive and impractical as image databases expand. This research presents a system for indexing CNN characteristics in order to enhance the efficiency of large-scale image search. The necessity to develop indexing frameworks capable of effectively extracting relevant photos from enormous datasets motivates this research. This framework contributes to the improvement of the performance of content-based image retrieval systems by establishing an efficient indexing framework for CNN features. The system draws on existing cutting-edge techniques to create an indexing framework capable of organizing and retrieving images based on similarities in the complex and multidimensional CNN feature space. To lower the computational cost and storage needs associated with large-scale picture searches, the system makes use of image features and hashing methods.