Content-based image retrieval for super-resolutioned images using feature fusion: Deep learning and hand crafted

An innovative image retrieval agenda by concatenating deep learning features from GoogleNet and low-level features from HSI and RGB color space is proposed in this article. Most of the CNN features suffer from loss of information due to image resize as a pre-processing stage. To reduce this information loss super-resolution technic is used for resizing images. An improved form of dot-diffused block truncation coding is used for extracting RGB handcraft features. To discover the interdependencies between color and intensity component of an image, interchannel voting between hue, saturation, and intensity component is calculated as a color feature in HSI space. Histogram of orientated gradient (HOG) feature is used as shape feature. Five standard performance parameters, average precision rate, average recall rate, F-Measure, Average Normalized Modified Retrieval Rank, and Total Minimum Retrieval Epoch, are applied on nine image datasets: Corel-1K, Corel-5K, Corel-10K, VisTex, STex, ColorBrodatz and three subsets of ImageNet dataset for evaluation process of proposed method. For all dataset the best performance is achieved by the proposed method with respect to all performance parameters.

Automated summary

This article proposes an innovative image retrieval method by combining deep learning features from GoogleNet and low-level features from HSI and RGB color space. The proposed method achieves the best performance in terms of various performance parameters on multiple image datasets.