Comprehensive Study of Indexing Techniques Used for Extracting CNN Features

With the widespread use of convolutional neural networks (CNNs) for image analysis and retrieval, it has become increasingly important to efficiently index and search the large feature space generated by these networks. This study provides a comprehensive review of various indexing techniques used for extracting CNN features. CNNs are commonly used in deep learning for image classification, object detection, and image retrieval. The main challenge in CNN-based image retrieval is the high-dimensional nature of the CNN features. Indexing techniques can be used to efficiently store and retrieve these features. In this study, different indexing techniques are discussed based on data structure, indexing scheme, feature type, and retrieval approach. This paper also provides an overview of the applications of these techniques, their pros and cons. The study will be useful for researchers and practitioners who want to use CNN features for image retrieval and need to select an appropriate indexing technique for their application.