Concept-based Object Segmentation for Video Retrieval Using Text-Vision Inference
碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 97 === In the issue of video retrieval, text terms and image feature of videos are important information for video content understanding. In recent years, researchers understand video content by searching high-level video concepts for similarity evaluation between vi...
Main Authors: | , |
---|---|
Other Authors: | |
Format: | Others |
Language: | zh-TW |
Published: |
2009
|
Online Access: | http://ndltd.ncl.edu.tw/handle/68251815056181382758 |
id |
ndltd-TW-097NCKU5392070 |
---|---|
record_format |
oai_dc |
spelling |
ndltd-TW-097NCKU53920702016-05-04T04:25:27Z http://ndltd.ncl.edu.tw/handle/68251815056181382758 Concept-based Object Segmentation for Video Retrieval Using Text-Vision Inference 影片檢索中應用文字與畫面推論作概念物件切割之研究 Shi-xin Mai 麥世昕 碩士 國立成功大學 資訊工程學系碩博士班 97 In the issue of video retrieval, text terms and image feature of videos are important information for video content understanding. In recent years, researchers understand video content by searching high-level video concepts for similarity evaluation between videos. Nevertheless, research related to video concept detection presently, has focused mainly on concept detection by concept detector directly, which neglects deletion of image background noise, result in inaccuracy of video concept detection. Therefore, the study exploits the news terms and news images to infer the concepts in the news image, and search concept-based objects by image segmentation. We hope to reduce the effect of image background noise, increase the accuracy of video retrieval system. The major research purpose of the thesis has four key points. 1.) Use news terms and news images for concept-based object segmentation which include that provide clue for image decomposition by text extension, search concept-based object by image decomposition, increase clue for image decomposition by concept extension. 2.) In text extension, provide more correlation clue for image decomposition by neighboring terms collection. 3.) In text extension, transform the clues into concepts by ontology mapping. 4.) In image decomposition, search the best segmentation result in image segmentation by Genetic Algorithm. In the experiment, we carry out the evaluation of image background noise, concept detector accuracy, image segmentation result, text extension and video retrieval system. According to the experimental results, we reduce the effect of image background noise for concept detection, and increase the accuracy of video retrieval system. Chung-Hsien Wu 吳宗憲 2009 學位論文 ; thesis 52 zh-TW |
collection |
NDLTD |
language |
zh-TW |
format |
Others
|
sources |
NDLTD |
description |
碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 97 === In the issue of video retrieval, text terms and image feature of videos are important information
for video content understanding. In recent years, researchers understand video content by searching
high-level video concepts for similarity evaluation between videos. Nevertheless, research related to
video concept detection presently, has focused mainly on concept detection by concept detector
directly, which neglects deletion of image background noise, result in inaccuracy of video concept
detection. Therefore, the study exploits the news terms and news images to infer the concepts in the
news image, and search concept-based objects by image segmentation. We hope to reduce the effect
of image background noise, increase the accuracy of video retrieval system.
The major research purpose of the thesis has four key points. 1.) Use news terms and news images
for concept-based object segmentation which include that provide clue for image decomposition by
text extension, search concept-based object by image decomposition, increase clue for image
decomposition by concept extension. 2.) In text extension, provide more correlation clue for image
decomposition by neighboring terms collection. 3.) In text extension, transform the clues into
concepts by ontology mapping. 4.) In image decomposition, search the best segmentation result in
image segmentation by Genetic Algorithm.
In the experiment, we carry out the evaluation of image background noise, concept detector
accuracy, image segmentation result, text extension and video retrieval system. According to the
experimental results, we reduce the effect of image background noise for concept detection, and
increase the accuracy of video retrieval system.
|
author2 |
Chung-Hsien Wu |
author_facet |
Chung-Hsien Wu Shi-xin Mai 麥世昕 |
author |
Shi-xin Mai 麥世昕 |
spellingShingle |
Shi-xin Mai 麥世昕 Concept-based Object Segmentation for Video Retrieval Using Text-Vision Inference |
author_sort |
Shi-xin Mai |
title |
Concept-based Object Segmentation for Video Retrieval Using Text-Vision Inference |
title_short |
Concept-based Object Segmentation for Video Retrieval Using Text-Vision Inference |
title_full |
Concept-based Object Segmentation for Video Retrieval Using Text-Vision Inference |
title_fullStr |
Concept-based Object Segmentation for Video Retrieval Using Text-Vision Inference |
title_full_unstemmed |
Concept-based Object Segmentation for Video Retrieval Using Text-Vision Inference |
title_sort |
concept-based object segmentation for video retrieval using text-vision inference |
publishDate |
2009 |
url |
http://ndltd.ncl.edu.tw/handle/68251815056181382758 |
work_keys_str_mv |
AT shixinmai conceptbasedobjectsegmentationforvideoretrievalusingtextvisioninference AT màishìxīn conceptbasedobjectsegmentationforvideoretrievalusingtextvisioninference AT shixinmai yǐngpiànjiǎnsuǒzhōngyīngyòngwénzìyǔhuàmiàntuīlùnzuògàiniànwùjiànqiègēzhīyánjiū AT màishìxīn yǐngpiànjiǎnsuǒzhōngyīngyòngwénzìyǔhuàmiàntuīlùnzuògàiniànwùjiànqiègēzhīyánjiū |
_version_ |
1718257327228846080 |