在影片檢索的議題上，影片的文字詞彙和畫面特徵是了解影片內容重要的資訊。近年來，研究者進一步藉由找尋更高層級的影片概念來了解影片內容，評估影片之間的相似程度。但是由於目前在偵測影片概念上相關的研究，大多直接利用概念偵測器於概念偵測上，而缺乏影像背景雜訊的去除，導致影片概念上的偵測不是那麼準確。因此，本研究利用新聞中的文字詞彙與新聞畫面作概念推論，並藉由畫面切割來找尋概念物件，希望能達到減少畫面中背景雜訊的影響，提高影片檢索正確率的目的。
本論文主要的研究目標分為三項重點：1.)利用文字詞彙與影像推論作概念性物件切割，其中包括藉由文字擴展提供影像切割上的線索，藉由影像切割來找出概念物件，藉由概念擴展來增加影像切割的線索;2.)在文字擴展中，藉由新聞中相鄰詞彙收集，提供影像切割上更具關連性的線索；3.) 在文字擴展中，藉由詞彙本體對應，將線索轉換成概念；4.)在影像切割中，利用基因演算法在畫面切
割上找尋出最佳的切割方法。
在實驗中，我們分別對影像背景雜訊、概念偵測器正確率、影像切割結果、文字與概念擴展和影片檢索進行評估，由實驗結果得知，本論文所提出的方法，確實能在概念偵測上減少影像背景雜訊的干擾，提高影片檢索的正確率。

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.

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