Image Categorization Based on Bag of Visual Words
碩士 === 義守大學 === 資訊工程學系 === 102 === In recent years, multimedia applications have quickly grown with the new technologies and innovations. Since the amounts of multimedia data are also increasing rapidly, the management and classification for multimedia data are becoming more and more important. In t...
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ndltd-TW-102ISU053920152015-10-14T00:23:51Z http://ndltd.ncl.edu.tw/handle/51583558192738651638 Image Categorization Based on Bag of Visual Words 以視覺字典為基礎的影像分類 Yao-sheng Sie 謝耀陞 碩士 義守大學 資訊工程學系 102 In recent years, multimedia applications have quickly grown with the new technologies and innovations. Since the amounts of multimedia data are also increasing rapidly, the management and classification for multimedia data are becoming more and more important. In this thesis, we first partition images to many blocks for extracting image features. The size of each block is 4 × 4 pixel. Based on the block content, we classify the blocks to two types of visual words: macro and micro visual words. After training procedure, we can derive macro and micro visual vocabulary, which are used to construct probability models for classification. Experimental results indicate that the probability models support effective performance for classification. The optimum weighting values of macro and micro are automatically adjusted. Experiment results show that the proposed method can effectively determine the proper ratio. Chung-Ming Kuo 郭忠民 2014 學位論文 ; thesis 107 zh-TW |
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碩士 === 義守大學 === 資訊工程學系 === 102 === In recent years, multimedia applications have quickly grown with the new technologies and innovations. Since the amounts of multimedia data are also increasing rapidly, the management and classification for multimedia data are becoming more and more important.
In this thesis, we first partition images to many blocks for extracting image features. The size of each block is 4 × 4 pixel. Based on the block content, we classify the blocks to two types of visual words: macro and micro visual words. After training procedure, we can derive macro and micro visual vocabulary, which are used to construct probability models for classification.
Experimental results indicate that the probability models support effective performance for classification. The optimum weighting values of macro and micro are automatically adjusted. Experiment results show that the proposed method can effectively determine the proper ratio.
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Chung-Ming Kuo |
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Chung-Ming Kuo Yao-sheng Sie 謝耀陞 |
author |
Yao-sheng Sie 謝耀陞 |
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Yao-sheng Sie 謝耀陞 Image Categorization Based on Bag of Visual Words |
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Yao-sheng Sie |
title |
Image Categorization Based on Bag of Visual Words |
title_short |
Image Categorization Based on Bag of Visual Words |
title_full |
Image Categorization Based on Bag of Visual Words |
title_fullStr |
Image Categorization Based on Bag of Visual Words |
title_full_unstemmed |
Image Categorization Based on Bag of Visual Words |
title_sort |
image categorization based on bag of visual words |
publishDate |
2014 |
url |
http://ndltd.ncl.edu.tw/handle/51583558192738651638 |
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