# Local Ternary Cross Structure Pattern: A Color LBP Feature Extraction with Applications in CBIR
## 6 Conclusions  
In this study, a series of color LBP descriptors namely local ternary cross structure pattern (LTCSP), uniform local ternary cross structure pattern (LTCSP\({}_{uni}\)) and rotation-invariant local ternary cross structure pattern (LTCSP\({}_{ri}\)) is proposed for the CBIR applications. According to the experimental results, the effectiveness, robustness, and practicability of the proposed descriptors are evaluated and compared on face, landmark, object, natural scene and textural image datasets. Based on these considerable results, it can be concluded that the proposed methods achieve a trade-off/compromise among notable retrieval accuracy, adaptive feature dimensionality and acceptable memory cost, and they can be considered as a competitive candidate for real-world CBIR applications. In the future, unsupervised feature selection [59] will be implemented to tackle the issue of the feature dimensionality and memory cost. In order to improve the robustness against illumination, the image normalization [60] will be exploited in the image pre-processing. In addition, manifold learning (ML) [61; 62] and query expansion (QE) [63] will also be considered to further enhance the retrieval performance. **Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/11/2211/s1](http://www.mdpi.com/2076-3417/9/11/2211/s1)]([http://www.mdpi.com/2076-3417/9/11/2211/s1](http://www.mdpi.com/2076-3417/9/11/2211/s1)). **Author Contributions:** [PERSON] conceived the research idea. [PERSON] and [PERSON] performed the experiments. [PERSON], [PERSON] and [PERSON] wrote the paper. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] gave many suggestions and helped revise this manuscript. **Funding:** This research was funded by the National Nature Science Foundation of China, grant number [61871106, 61370152], the Fundamental Research Grant Scheme for the Central Universities, grant number [130204003], the Project of Shandong Province Higher Educational Science and Technology Program, grant number [I16 LN68], the Weifang Science and Technology Development Plan Project (Nos. 2017 GX006, 2018 GX009, 2018 GX004) and the National Key Technology Research and Development Programme of the Ministry of Science and Technology of China, grant number [2014 BAI17B02]. **Conflicts of Interest:** The authors declare no conflict of interest. Figure 8: The comparisons of the APR rates among the proposed LTCSP, LTCSP\({}_{uni}\) and LTCSP\({}_{ri}\) descriptors and the emerging deep learning (DL)-based models on six datasets. In order to illustrate the advantage of the proposed five-level color quantizer (FLCQ), the mean square error (MSE) values of the EICQ and FLCQ in the CIELAB color model are compared on all six datasets. To guarantee the experimental fairness, the same settings are set to the EICQ and FLCQ quantizers, apart from the number of quantization intervals. The lowest MSE values of the FLCQ and EICQ quantizers are highlighted in bold. Firstly, from Tables A1-A6, it can be summarized that the MSE values of the FLCQ quantizer are lower than EICQ on all six datasets. Secondly, it can be concluded that the MSE values of the FLCQ and FLCQ quantizers are lower than the corresponding MSE values of the FLCQ and FLCQ quantizers.