Automatic Semantic Image Classification and Retrieval Based on the Weighted Feature Algorithm
Organizing images into meaningful (semantically) categories using low-level visual features is a challenging and important problem in content-based image retrieval. Clustering algorithms make it possible to represent visual features of images with finite symbols. However, there are two problems in most current image clustering algorithms. One is without considering the choice of the initial cluster centers which have a direct impact on the formation of final clusters, and the other is without considering the relevant features and assigning equal weights to these feature dimensions. According to the two problems we propose a weighted features algorithm. First, we use the labeled image sam pies to calculate the weight for each feature according to the feature degree of discrete. These weighted features have been used to calculate the initial cluster centers because they can well represent the cluster. Then, we use the weighted features(based on the different image data set) algorithm to discard the irrelevant features and reduce the feature dimensions through the whole clustering process. Experimental results and comparisons are given to illustrate the performance of the new algorithm.
semantic image classification content-based image retrieval cluster weighted feature
Keping Wang Xiaojie Wang Ke Zhang Yixin Zhong
Department of Computer Science, Beijing University of Posts and Telecommunications Beijing, 100110, Department of Computer Science, Beijing University of Posts and Telecommunications Beijing, 100110,
国际会议
The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)
沈阳
英文
87-91
2010-03-27（万方平台首次上网日期，不代表论文的发表时间）