鲁棒视觉词汇本的自适应构造与自然场景分类应用  

An Adaptive Algorithm for Robust Visual Codebook Generation and Its Natural Scene Categorization Application

作　　者：杨丹[1] 李博[2] 赵红[1] 

机构地区：[1]重庆大学软件学院,重庆400030 [2]重庆大学计算机学院,重庆400030

出　　处：《电子与信息学报》2010年第9期2139-2144,共6页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(60975015);教育部博士点基金(20090191110023);重庆市科技攻关项目(CSTC2009AC2057)资助课题

摘　　要：该文提出了一种视觉词汇本的优化构造策略。首先引入条件数定量评估海量低层特征的稳定性,排除病态特征,筛选稳定的鲁棒视觉特征;通过分析聚类和降维的内在联系,构造了具有聚类结构的视觉特征自适应降维算法;进而利用低维聚类结构信息中的邻域支持度,自适应选取最佳的初始视觉词汇,同时选择Sil指标作为目标函数,从而改进流行的LBG词汇本生成算法敏感于初始点的随机选取,并只能得到局部最优等不足。新的视觉词汇本生成算法具有聚类和降维的统一计算功能、良好的鲁棒性和自适应优化等特性。基于概率潜在语义分析技术将该文的视觉词汇本应用于自然场景分类,在13类场景图像库上取得了73.46%的平均分类率。This paper describes a novel optimization framework for visual codebook generation. Firstly,the Condition Number （CN） is applied to evaluate the stability of initial visual features,and the well conditioned features are preserved by eliminating the bad ones. At the mean time,an adaptive algorithm to generate low-dimensional visual words is proposed by studying the relationship between clustering and dimension-reducing. In order to overcome the popular LBG codebook design algorithm suffers from local optimality and is sensitive to the initial solution,a parameter called neighborhood-support for each feature is calculated according to clustering structure,which is used to select initial visual words adaptively. Finally,the rational distortion function is redefined using Silhouette. Compared with traditional algorithm,the presented algorithm has excellent properties at simultaneous clustering and dimension reduction,good robustness and adaptive optimization. A good performance （73.46% classification rate） of application this method to 13-Scene classification is obtained by using Probabilistic Latent Semantic Analysis （PLSA）.

关 键 词：模式识别 自然场景分类 视觉词汇本 条件数 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]