Content-based image retrieval using feature-fusion of GroupNormalized-Inception-Darknet-53 features and handcraft features

Abstract

This paper proposed an efficient image retrieval framework by feature-fusion of high-level features from the improved version of DarkNet-53, named GroupNormalized-Inception-Darknet-53 (GN-Inception-Darknet-53), and handcraft features extracted from both RGB and HSI color models. To extract the more detailed features of an image, we augmented one inception layer, which includes 1 × 1, 3 × 3, and 5 × 5 kernels in place of an existing 3 × 3 kernel. To make the normalization process of the proposed model less dependent on batch size, Group Normalization (GN) layer is used instead of Batch Normalization (BN). A modified version of dot-diffused block truncation coding (DDBTC) is used to extract handcraft features in RGB color space. For HSI color space, interchannel voting between hue, saturation, and intensity components is used as color feature. To extract shape features histogram of orientated gradient (HOG) is applied on RGB color space. To evaluate the efficiency of our proposed method, Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are calculated for Corel-1 K, Corel-5 K, Corel-10 K, VisTex, Stex & Color Brodatz datasets. In all datasets, the proposed method shows the best results for all the instances with a minimum average improvement of 7.02%.