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Research on content-based image retrieval (CBIR) has been under developmentfor decades, and numerous methods have been competing to extract the mostdiscriminative features for improved representation of the image content.Recently, deep learning methods have gained attention in computer vision,including CBIR. In this paper, we present a comparative investigation ofdifferent features, including low-level and high-level features, for CBIR. Wecompare the performance of CBIR systems using different deep features withstate-of-the-art low-level features such as SIFT, SURF, HOG, LBP, and LTP,using different dictionaries and coefficient learning techniques. Furthermore,we conduct comparisons with a set of primitive and popular features that havebeen used in this field, including colour histograms and Gabor features. Wealso investigate the discriminative power of deep features using certainsimilarity measures under different validation approaches. Furthermore, weinvestigate the effects of the dimensionality reduction of deep features on theperformance of CBIR systems using principal component analysis, discretewavelet transform, and discrete cosine transform. Unprecedentedly, theexperimental results demonstrate high (95\% and 93\%) mean average precisionswhen using the VGG-16 FC7 deep features of Corel-1000 and Coil-20 datasets with10-D and 20-D K-SVD, respectively.

Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study