Local Orientation Gradient XOR Patterns: A New Feature Descriptor for Image Indexing and Retrieval

A. Hariprasad Reddy*, N. Subhash Chandra**
* Research Scholar, Department of Computer Science and Engineering, JNTU Hyderabad, Andhra Pradesh, India.
** Principal & Professor, Holy Mary Institute of Technology and Science, Hyderabad, Andhra Pradesh, India.
DOI : https://doi.org/


This paper presents a novel feature extraction method, Local Orientation Gradient XoR Patterns (LOGXoRP) for image indexing and retrieval. The LOGXoRP encodes the exclusive OR (XOR) operation between the center pixel and its surrounding neighbors of quantized orientation and gradient values, whereas the Local Binary Patterns (LBP) and the Local Gradient Patterns (LGP) encode the relationship between the gray values of center pixel and its neighbors. The authors shows that the LOGXoRP can extract effective texture (edge) features as compared to LBP and LGP. The performance of the proposed method is tested by conducting two experiments on Corel-5K and Corel-10K databases. The results of the proposed method after being investigated shows a significant improvement in terms of their evaluation measures as compared to LBP, LGP and other existing state-of-art techniques on respective databases.


Feature Extraction, Local Binary Patterns (LBP), Local Gradient Pattern (LGP), Content Based Image Retrieval (CBIR), Texture.

How to Cite this Article?

Reddy, A. H., and Chandra, N. S. (2015). Local Orientation Gradient XOR Patterns: A New Feature Descriptor for Image Indexing and Retrieval. i-manager’s Journal on Pattern Recognition, 2(4), 1-10.


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