Mathematical Problems in Engineering / 2019 / Article / Tab 2

Review Article

Content-Based Image Retrieval and Feature Extraction: A Comprehensive Review

Table 2

A summary of the performance of texture features.

AuthorsDatasetsPurposeModelPerformance/accuracy

Papakostas et al. [32]COIL, ORL, JAFFE, TRIESCH IWavelet moments and their corresponding invariants in machine vision systemWavelet moments and moment invariantsClassification performances on (100%) percent of entire data are 0.3083, 0.2425, 0.1784, and 0.1500, respectively, for datasets
Wang et al. [34]Corel-1000 and Corel-10000Image retrievalSEDSimilarity between query image and image database is 3.9198, 9.92209, and 8.86239 for dragons, busses, and landscapes, and there will be high precision rate when the query image has noteworthy regions or texture
Liu et al. [33]Corel datasets (Corel-5000 and Corel-10000)Image retrievalMSDAverage retrieval precision and recall ratios on Corel-5000 and Corel-10000 are 55.92%, 6.71% and 41.44%, 5.48%
Lasmar and Berthoumieu [40]Vistex, Brodatz, ALOTTexture image retrievalGC-MGG and GC-MWblImprovement in average retrieval rate on Brodatz (EB2) by our model is 6.86% and 5.23%, respectively, with Daubechies filter db4 and dual-tree complex wavelet transform
Fadaei et al. [38]Brodatz and VistexContent-based image retrievalLDRP80.81% and 91.91% are average precision rates of the first-order LDRP(, ) for the respective datasets