TY  - JOUR
T1  - An Efficient Approach for Visual Object Categorization based on
Enhanced Generalized Gabor Filter and SVM Classifier
AU - Ayad, Hayder AU - Sheikh Abdullah, Siti Norul Huda AU - Ahmed Hadi, Raad AU - Jassim Mohammed, Mamoun AU - Edwar George, Loay 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 16
SP  - 5753
EP  - 5761
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.5753.5761
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.5753.5761
KW  - Gabor filter
KW  -VOC technique
KW  -SVM classifier
KW  -Naive combination approach
KW  -unsupervised
KW  -benchmark
AB  - 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.
ER  -