Authors:
Ahmad Bitar
;
Mohammad M. Mansour
and
Ali Chehab
Affiliation:
American University of Beirut, Lebanon
Keyword(s):
HMAX, Support Vector Machine, Nearest Neighbor, Caltech101.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
In this paper, an efficient implementation for a recognition system based on the original HMAX model of the visual cortex is proposed. Various optimizations targeted to increase accuracy at the so-called layers S1, C1, and S2 of the HMAX model are proposed. At layer S1, all unimportant information such as illumination and expression variations are eliminated from the images. Each image is then convolved with 64 separable Gabor filters in the spatial domain. At layer C1, the minimum scales values are exploited to be embedded into the maximum ones using the additive embedding space. At layer S2, the prototypes are generated in a more efficient way using Partitioning Around Medoid (PAM) clustering algorithm. The impact of these optimizations in terms of accuracy and computational complexity was evaluated on the Caltech101 database, and compared with the baseline performance using support vector machine (SVM) and nearest neighbor (NN) classifiers. The results show that our model provides
significant improvement in accuracy at the S1 layer by more than 10% where the computational complexity is also reduced. The accuracy is slightly increased for both approximations at the C1 and S2 layers.
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