一种基于特征整合理论的物体识别模型

doi:10.7523/j.issn.2095-6134.2012.3.018

中国科学院大学学报 ›› 2012, Vol. ›› Issue (3): 399-405.DOI: 10.7523/j.issn.2095-6134.2012.3.018

一种基于特征整合理论的物体识别模型

王喜顺^1,2, 刘曦^1,2, 史忠植², 隋红建¹

1. 中国科学院研究生院, 北京 100049;
2. 中国科学院计算技术研究所, 北京 100190

收稿日期:2010-10-13 修回日期:2011-03-04 发布日期:2012-05-15
基金资助:
Supported by the National Basic Research Priorities Programme (2007CB311004), National Science and Technology Support Plan (2006BAC08B06) and National Science Foundation of China (60775035, 60903141, 60933004, 60970088, 61035003)

A new object recognition model based on feature integration theory

WANG Xi-Shun^1,2, LIU Xi^1,2, SHI Zhong-Zhi², SUI Hong-Jian¹

1. Graduate University, Chinese Academy of Sciences, Beijing 100049, China;
2. Key Laboratory of Intelligent Information Processing,Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Received:2010-10-13 Revised:2011-03-04 Published:2012-05-15
Supported by:
Supported by the National Basic Research Priorities Programme (2007CB311004), National Science and Technology Support Plan (2006BAC08B06) and National Science Foundation of China (60775035, 60903141, 60933004, 60970088, 61035003)

摘要/Abstract

摘要： 基于认知科学的研究提出一个新颖的计算模型用于物体识别.特征整合理论为计算模型提供了总体路线.基于最大熵原理构建学习过程,获得必要的先验知识构成认知网络.利用认知网络,将底层的图像特征和高层知识捆绑起来.利用条件随机场的基本概念和原理建模捆绑过程.将计算模型应用于现实世界的物体识别,在标准图像库上进行评估,取得了很好的效果.

关键词: 条件随机场, 特征捆绑, 特征整合, 物体识别

Abstract: We propose a new computational model for object recognition based on the vision cognitive findings. Feature integration theory offers the roadmap for our computing model. We construct the learning procedure to acquire necessary pre-knowledge for the recognition network on the basis of the hypothesis-maximum entropy principle. With the recognition network, we can bind the low-level image features and the high-level knowledge. Fundamental concepts and principles of conditional random fields are employed to model the binding process. We apply our model to real object recognition problem and evaluate it on the benchmark image databases to show its satisfactory performance.

Key words: conditional random fields, feature binding, feature integration, object recognition

中图分类号:

TP181

王喜顺, 刘曦, 史忠植, 隋红建. 一种基于特征整合理论的物体识别模型[J]. 中国科学院大学学报, 2012, (3): 399-405.

WANG Xi-Shun, LIU Xi, SHI Zhong-Zhi, SUI Hong-Jian. A new object recognition model based on feature integration theory[J]. , 2012, (3): 399-405.

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一种基于特征整合理论的物体识别模型

A new object recognition model based on feature integration theory

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