A Data Mining Approach to Object recognition
Date Issued
2005
Date
2005
Author(s)
Su, Songkai
DOI
en-US
Abstract
The goal of object recognition is to identify the object in an image and objects are classified into several categories which shares a common feature. In this thesis, we proposed a data mining approach consisting of three phases to realize object recognition by a means of mining feature patterns. First, high curvature points are extracted from the contour of an object and the distances between two points and the curvatures at these points are recoded to form a sequence to represent the shape of this object. In the second phase, Apriori algorithm is used to discover the maximal frequent patterns as feature patterns of every category. A fuzzy concept is included into the process of mining feature patterns to tolerance slight transformations. Finally, matching is done by approximate sequential matching which is a dynamic programming problem. If the number of mismatch items between a feature pattern and a sequence is no more than a user-defined threshold, we say the feature pattern is matched with the sequence. Thus, the object of this sequence is classified into the category of this feature pattern.
Experimental results show that our method performs better than a landmark-based method and does not require many training dataset to identify objects with similar poses.
Subjects
物件辨識
序列模式探勘
序列相似性
object recognition
sequential pattern mining
sequence similarity
Type
other
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