Automatic Semantic Topic Discovery Approach of the Line Image Based on Support Vector Machine
Abstract: A semantic topic discovery approach of the line image, based on support vector machine, has been proposed in this paper. Firstly, the training images are divided into non-overlapping sub- blocks with same size. After clustering image sub-blocks, we obtained class set generated by cluster centers, and extracted all nouns from text annotation of each training image in order to obtain a keyword set. Secondly, the un-label testing image is also divided into non-overlapping sub-blocks as same as training images, we calculated the correlation between the sub-block and each keyword, and a keywords set for each sub-block may be obtained. Finally, the number of each keyword appearing in the each sub-block is calculated, we let the keywords with maximum to occurrences number be the semantic topics of the line image. The experimental results confirm that proposed automatic semantic topic discovery approach for line image is effective and has good performance.
文章引用: 金 聪 , 刘金安 (2014) 基于支持向量机的线条图像语义主题自动发现方法。 图像与信号处理， 3， 78-85. doi: 10.12677/JISP.2014.33011
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