A deep learning content-based image retrieval approach using cloud computing

Due to the rapid growth in multimedia content and its visual complexity, contentbased image retrieval (CBIR) has become a very challenging task. Existing works achieve high precision values at first retrieval levels such as top 10 and top 20 images, but low precision values at subsequent levels such as top 40, 50, and 70, so the goal of this paper is to propose a new CBIR approach that achieves high precision values at all retrieval levels. The proposed method combines features extracted from the pre-trained AlexNet model and discrete cosine transform (DCT). Then principal components analysis (PCA) is performed on AlexNet’s features and feeding these combination to multiclass support vector machine (SVM). The euclidean distance is used to measure the similarity between query and stored images features within the predicted class by SVM. Finally top similar images are ranked and retrieved. All above techniques require huge computational power which may not be available on client machine thus, the processing of these tasks is processed on cloud. Experimental results on the benchmark Corel-1k show that the proposed method achieves high precision value 97% along all retrieval levels top 10, 20 and 70 images and requiring less memory compared to other methods.