  Research on content-based image retrieval (CBIR) has been under development
for decades, and numerous methods have been competing to extract the most
discriminative 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 of
different features, including low-level and high-level features, for CBIR. We
compare the performance of CBIR systems using different deep features with
state-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 have
been used in this field, including colour histograms and Gabor features. We
also investigate the discriminative power of deep features using certain
similarity measures under different validation approaches. Furthermore, we
investigate the effects of the dimensionality reduction of deep features on the
performance of CBIR systems using principal component analysis, discrete
wavelet transform, and discrete cosine transform. Unprecedentedly, the
experimental results demonstrate high (95\% and 93\%) mean average precisions
when using the VGG-16 FC7 deep features of Corel-1000 and Coil-20 datasets with
10-D and 20-D K-SVD, respectively.
