Improving invariance in visual classification with biologically inspired mechanism

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 机器人应用与理论组

	Improving invariance in visual classification with biologically inspired mechanism
	Tang, Tang; Qiao, Hong
刊名	NEUROCOMPUTING
	2014-06-10
卷号	133 页码:328-341
关键词	Biologically inspired Visual classification Max-pooling Template matching
英文摘要	A computational model of visual cortex has raised great interest in developing algorithms mimicking human visual systems. The max-operation is employed in the model to emulate the scale and position invariant responses of the visual cells. We further extend this idea to enhance the tolerance of visual classification against the general intra-class variability. A general architecture of the basic block constituting the model is first presented. The architecture adaptively chooses the best matching template from a set of competing templates to predict the label of the incoming sample. To optimize the non-convex and non-smooth objective function resulted, we develop an algorithm to train each template alternately. Experiments show that the proposed method significantly outperforms linear classifiers as a template matching method in several image classification tasks, and is much more computationally efficient than other commonly used non-linear classifiers. In the image classification task on the Caltech 101 database, the performance of the biologically inspired model is obviously boosted by incorporating the proposed method. (C) 2014 Elsevier B.V. All rights reserved.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence
研究领域[WOS]	Computer Science
关键词[WOS]	OBJECT RECOGNITION ; FACE DETECTION ; RECEPTIVE-FIELDS ; BACK-PROPAGATION ; COMPUTER VISION ; FEATURES ; CORTEX
收录类别	SCI
语种	英语
WOS记录号	WOS:000334481400032
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3039]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位	Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Tang, Tang,Qiao, Hong. Improving invariance in visual classification with biologically inspired mechanism[J]. NEUROCOMPUTING,2014,133:328-341.
APA	Tang, Tang,&Qiao, Hong.(2014).Improving invariance in visual classification with biologically inspired mechanism.NEUROCOMPUTING,133,328-341.
MLA	Tang, Tang,et al."Improving invariance in visual classification with biologically inspired mechanism".NEUROCOMPUTING 133(2014):328-341.