Quality Aware Content-Based Image Retrieval Using QIM and Deep Learning for Big Visual Data

Modern technology has made storing, sharing, and organizing huge amounts of data simple through the Internet of Things. Search engines and query-based retrieval databases made access to relevant data easy through ranking and indexing based on stored content. In this paper, a secure CBIR scheme based on watermarking is proposed. Firstly, the image owner embeds the watermark in the image using quantization index modulation (QIM) in the luminance (Y) color space. The watermarked images are then uploaded to the cloud server, which extracts image feature vectors. In this article, features derived from a pre-trained network model from a deep-learning convolutional neural network trained for large image classification have been used for the retrieval of similar images. The image similarity is calculated using Euclidean distance, and the precision (P) is used as the performance measure of the model that achieved nearly 100%. Extensive experiments are carried out, and assessment results reveal the outperforming result of the proposed technique compared to other related schemes. The scheme can be used in many applications that need CBIR, such as digital libraries, historical research, fingerprint identification, and crime prevention.