Object recognition using k-nearest neighbor in object space

Authors:
Jong-Min Kim;Jin-Kyoung Heo;Hwan-Seok Yang;Mang-Kyu Song;Seung-Kyu Park;Woong-Ki Lee
Affiliations:
Computer Science and Statistic Graduate School, Chosun University, Korea;Division of Cyber Investigation Police, Howon University, Korea;Computer Science and Statistic Graduate School, Chosun University, Korea;Computer Science and Statistic Graduate School, Chosun University, Korea;Division of Cyber Investigation Police, Howon University, Korea;Computer Science and Statistic Graduate School, Chosun University, Korea
Venue:
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
Year:
2006

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Abstract

Object recognition technologies using PCA(principal component analysis) recognize objects by deciding representative features of objects in the model image, extracting feature vectors from objects in an image and measuring the distance between them and object representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the K-nearest neighbor technique(class-to-class) in which a group of object models of the same class is used as recognition unit for the images inputted on a continual input image. However, we propose the object recognition technique new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. Object recognition algorithm proposed here represents more enhanced recognition rate to change of illumination than existing methods.