A Novel Content-Based Image Retrieval System with Feature Descriptor Integration and Accuracy Noise Reduction.

Efficient algorithms, intelligent approaches, and intensive use of computers are crucial to extracting reliable information from large-scale data sets. It is the case, for example, in retrieving information from multimedia data. As different systems integrate individual electronic devices such as smartphones and cameras into storage, sharing, and social media platforms, the amount of multimedia data has increased dramatically in recent years. Therefore, content-based image retrieval (CBIR) is quite challenging. In this paper, we introduce a new image descriptors integration to represent the visual attributes of images. Furthermore, we noticed a pattern between different CBIR systems in which the first images retrieved are more likely to be assertive than those in the middle and final positions. Then, we investigated the interactions between the first images retrieved from CBIR systems to present a novel method for reducing accuracy noise. The performance of the proposed method is evaluated in different data sets (Corel-1K, Corel-5K, Corel-10K, GHIM-10K) and compared to related works. The experimental results demonstrate that the proposal consistently retrieves images with similar content. The code prepared by the authors is publicly available.