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Recently, the increasing use of mobile devices, such as cameras and smartphones, has resulted in a dramatic increase in the amount of images collected every day. Therefore, retrieving and managing these large volumes of images has become a major challenge in the field of computer vision. One of the solutions for efficiently managing image databases is an Image Content Search (CBIR) system. For this, we introduce in this chapter some fundamental theories of content-based image retrieval for large scale databases using Parallel frameworks. Section 2 and Section 3 presents the basic methods of content-based image retrieval. Then, as the emphasis of this chapter, we introduce in Section 1.2 A content-based image retrieval system for large-scale images databases. After that, we briefly address Big Data, Big Data processing platforms for large scale image retrieval. In Sections 5, 6, 7, and 8. Finally, we draw a conclusion in Section 9.

This article describes how the classical algorithm of shape context (SC) is still unable to capture the part structure of some complex shapes. To overcome this insufficiency, the authors propose a novel shape-based retrieval approach that is called HybMAS-GA using a multi-agent system (MAS) and a genetic algorithm (GA). They define a new distance called approximate distance (AD) to define a SC method by AD, which called approximate distance shape context (ADSC) descriptor. Furthermore, the authors' proposed HybMAS-GA is a star architecture where all shape context agents, N, are directly linked to a coordinator agent. Each retrieval agent must perform either a SC or an ADSC method to obtain a similar shape, started from its own initial configuration of sample points. This combination increases the efficiency of the proposed HybMAS-GA algorithm and ensures its convergence to an optimal images retrieval as it is shown through experimental results.