@article{MAKHILLJEAS201914717635,
    title = {Local Quadrant Pattern with Co-occurrence Matrix (LQP-CM): Hybrid Method for
Image Classification and Feature Extraction},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {7},
    pages = {2171-2176},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.2171.2176},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.2171.2176},
    author = {Hassan Mohammed and},
    keywords = {Local quadrant pattern,local quadrant pattern with co-occurrence matrix,local binary pattern,local
ternary pattern,gray-level co-occurrence matrix,classification,Euclidean distance},
    abstract = {Image classification is important in several fields which depend on the methods of extracting
the features. This study proposes a new method for features extraction called Local Quadrant Pattern with
Co-occurrence Matrix (LQP-CM) that related with Local Ternary Pattern (LTP) and Gray-Level Co-occurrence
Matrix (GLCM). LQP-CM will map the values into four types instead of two like Local Binary Pattern (LBP) or
three like LTP. For classification, this study will use the Euclidean Distance (ED) to classifying the features that
extracting. The data set that used in this study is Brodatz dataset. The MATLAB environment was adopted
in the programming and the criteria was used to evaluate the performance of the proposed method is percentage
of correct classification which proved successful in classification the database used in high efficiency.}
    }