Enhanced HMAX model with feedforward feature learning for multiclass categorization
Li, Yinlin1; Wu, Wei1; Zhang, Bo2; Li, Fengfu2
Source PublicationFRONTIERS IN COMPUTATIONAL NEUROSCIENCE
2015-10-07
Volume9Pages:1-14
SubtypeArticle
AbstractIn recent years, the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields. Many biologically inspired visual models are proposed, and among them, the Hierarchical Max-pooling model (HMAX) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal (PIT) layer of the primate visual cortex, which could generate a series of position- and scale- invariant features. However, it could be improved with attention modulation and memory processing, which are two important properties of the primate visual cortex. Thus, in this paper, based on recent biological research on the primate visual cortex, we still mimic the first 100-150 ms of visual cognition to enhance the HMAX model, which mainly focuses on the unsupervised feedforward feature learning process. The main modifications are as follows: (1) To mimic the attention modulation mechanism of V1 layer, a bottom-up saliency map is computed in the Si layer of the HMAX model, which can support the initial feature extraction for memory processing; (2) To mimic the learning, clustering and short-term memory to long-term memory conversion abilities of V2 and IT, an unsupervised iterative clustering method is used to learn clusters with multiscale middle level patches, which are taken as long-term memory; (3) Inspired by the multiple feature encoding mode of the primate visual cortex, information including color, orientation, and spatial position are encoded in different layers of the HMAX model progressively. By adding a softmax layer at the top of the model, multiclass categorization experiments can be conducted, and the results on Caltech101 show that the enhanced model with a smaller memory size exhibits higher accuracy than the original HMAX model, and could also achieve better accuracy than other unsupervised feature learning methods in multiclass categorization task.
KeywordHmax Biologically Inspired Feedforward Saliency Map Middle Level Patch Learning Feature Encoding Multiclass Categorization
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
DOI10.3389/fncom.2015.00123
WOS KeywordHUMAN EXTRASTRIATE CORTEX ; INFERIOR TEMPORAL CORTEX ; PRIMATE VISUAL-CORTEX ; OBJECT RECOGNITION ; RECEPTIVE FIELDS ; STRIATE CORTEX ; VISION ; DISCRIMINATION ; CLASSIFICATION ; CONNECTIONS
Indexed BySCI ; SSCi
Language英语
WOS Research AreaMathematical & Computational Biology ; Neurosciences & Neurology
WOS SubjectMathematical & Computational Biology ; Neurosciences
WOS IDWOS:000362659000001
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Cited Times:41[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10730
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Affiliation1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Appl Math, Acad Math & Syst Sci, Beijing 100190, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Yinlin,Wu, Wei,Zhang, Bo,et al. Enhanced HMAX model with feedforward feature learning for multiclass categorization[J]. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,2015,9:1-14.
APA Li, Yinlin,Wu, Wei,Zhang, Bo,&Li, Fengfu.(2015).Enhanced HMAX model with feedforward feature learning for multiclass categorization.FRONTIERS IN COMPUTATIONAL NEUROSCIENCE,9,1-14.
MLA Li, Yinlin,et al."Enhanced HMAX model with feedforward feature learning for multiclass categorization".FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 9(2015):1-14.
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