Paper Info
Title
Biological modeling of human visual system for object recognition using GLoP filters and sparse coding on multi-manifolds.
Abstract
Hierarchical MAX model (HMAX) is a bio-inspired model mimicking the visual information processing of visual cortex. However, the visual processing of lower level, such as retina and lateral geniculate nucleus (LGN), is not concerned, and the properties of higher-level neurons are not sufficiently specified. Given that, we develop an extended HMAX model, denoted as E-HMAX, by the following biologically plausible ways. First, contrast normalization is conducted on the input image to simulate the processing of human retina and LGN. Second, log-polar Gabor (GLoP) filters are used to simulate the properties of V1 simple cells instead of Gabor filters. Then, sparse coding on multi-manifolds is modeled to compute the V4 simple cell response instead of Euclidean distance. Meanwhile, a template learning method based on dictionary learning on multi-manifolds is proposed to select informative templates during template learning stage. Experimental results demonstrate that the proposed model has greatly outperformed the standard HMAX model. It is also comparable to some state-of-the-art approaches such as EBIM and OGHM-HMAX.
Year
DOI
Venue
2018
10.1007/s00138-018-0928-9
Mach. Vis. Appl.
Keywords
Field
DocType
Biological system modeling, HMAX, GLoP filters, Sparse coding on multi-manifolds
Computer vision,Normalization (statistics),Visual processing,Pattern recognition,Visual cortex,Computer science,Human visual system model,Neural coding,Simple cell,Euclidean distance,Artificial intelligence,Cognitive neuroscience of visual object recognition
Journal
Volume
Issue
ISSN
29
6
0932-8092
Citations 
PageRank 
References 
1
0.35
26
Authors
5
Name
Order
Citations
PageRank
Limiao Deng151.81
Yanjiang Wang2158.65
Bao-Di Liu316627.34
Weifeng Liu48713.82
Yujuan Qi592.19