@article{MAKHILLJEAS2019141618200,
    title = {An Efficient Approach for Visual Object Categorization based on
Enhanced Generalized Gabor Filter and SVM Classifier},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {16},
    pages = {5753-5761},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.5753.5761},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.5753.5761},
    author = {Hayder,Siti Norul Huda,Raad,Mamoun and},
    keywords = {Gabor filter,VOC technique,SVM classifier,Naive combination approach,unsupervised,benchmark},
    abstract = {Filter banks such as the Gabor Filter (GF) are widely used to describe objects. The main disadvantage
of the Gabor filter is that it constructs redundant and incompact filters that may decrease system recognition
performance. The purpose of the current study primarily is to enhance the categorization problem through
generalizing the GF method (GGF). The unsupervised machine learning algorithm, denoted by the k-means
clustering algorithm is proposed to implement generalization on a GF set. To assess the performance of the
proposed method, the standard GF is used as a benchmark. Furthermore, the first 20 classes and the overall
classes from the dataset Caltech 101 have been utalized in the performance demonstration of the newly
suggested method. Based on a single classifier and combination feature (Naive approach), the proposed GGF
outperforms and shows higher potential results than the standard GF in describing objects.}
    }