Mathematical Problems in Engineering / 2019 / Article / Tab 3 / Review Article
Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review Table 3 A summary of the performance of fusion feature-based approaches for CBIR.
Author Dataset Images/classes Techniques Applications Precision Nazir et al. [63 ] Corel 1-K 1000 images which are divided into 10 classes HSV color histogram, discrete wavelet transform, and edge histogram descriptor Content-based image retrieval 0.735 Ashraf et al. [56 ] Corel 1000 It contains 10 categories. Each category contains 100 images with different size Multimedia data for content-based image retrieval by using multiple features Content-based image retrieval 0.875 Mistry et al. [57 ] Wang Dataset contains 1000 images from 10 different classes Hybrid features and various distance metric Content-based image retrieval 0.875 Ahmed et al. [58 ] Corel-1000 Dataset contains 1000 image splitted into 10 categories. Each category consists of 100 images Image features information fusion Content-based image retrieval For Africa and building categories, the precision is 0.90 Liu et al. [59 ] Brodatz, Vistex Brodatz consisting of 1856 and 600 texture image Vistex consisting of 640 and 864 texture images. Each class in Brodatz and Vistex consists of 16 similar images Fusion of color histogram and LBP-based features Texture-based images retrieval 0.841 and 0.952