Content-Based Image Retrieval Using Deep Features and Hamming Distance

R. T. Akash Guna, O. K. Sikha

Abstract: Content-based image retrieval is a method of retrieving images based on its semantic features. Classical CBIR models in the literature uses low-level features like color, shape, and texture to retrieve similar images from the database. Low-level features work well when the dataset is small; however, it fails to reduce the semantic gap when the image database becomes large and diverse. In the recent past, many researchers have exploited the usage of pretrained neural network as a feature extractor capable of giving a high-level feature representation for images. In this chapter, we use the output vectors of intermediate layers of an Inception Resnet deep learning model as the feature representation for image retrieval. The high-level feature vectors obtained from the deep layers of Inception Resnet are clustered using K-means clustering algorithm to make the retrieval process faster. The second part of the chapter explores various distance metrics to find most similar images corresponding to the query image. Based on the analysis on various distance measures, it is found that Hamming distance performs better than Euclidean distance in retrieving similar images when using deep features owing to the sparse nature of the feature vectors produced by the intermediate deep layers of Inception Resnet.