The process of image retrieval presents an interesting tool for different domains related\nto computer vision such as multimedia retrieval, pattern recognition, medical imaging, video\nsurveillance and movements analysis. Visual characteristics of images such as color, texture and shape\nare used to identify the content of images. However, the retrieving process becomes very challenging\ndue to the hard management of large databases in terms of storage, computation complexity, temporal\nperformance and similarity representation. In this paper, we propose a cloud-based platform in which\nwe integrate several features extraction algorithms used for content-based image retrieval (CBIR)\nsystems. Moreover, we propose an efficient combination of SIFT and SURF descriptors that allowed\nto extract and match image features and hence improve the process of image retrieval. The proposed\nalgorithms have been implemented on the CPU and also adapted to fully exploit the power of GPUs.\nOur platform is presented with a responsive web solution that offers for users the possibility to\nexploit, test and evaluate image retrieval methods. The platform offers to users a simple-to-use access\nfor different algorithms such as SIFT, SURF descriptors without the need to setup the environment\nor install anything while spending minimal efforts on preprocessing and configuring. On the other\nhand, our cloud-based CPU and GPU implementations are scalable, which means that they can be\nused even with large database of multimedia documents. The obtained results showed: 1. Precision\nimprovement in terms of recall and precision; 2. Performance improvement in terms of computation\ntime as a result of exploiting GPUs in parallel; 3. Reduction of energy consumption.
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