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Research on Annotation Technology of Face Images in Network News

Author: LiuShengYu
Tutor: TangJiangLong
School: Harbin Institute of Technology
Course: Computer Science and Technology
Keywords: Network News Names Face image Face annotation
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 35
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Abstract


With multimedia technology, the rapid development of computer technology, multimedia data has become an important source of information, people every day through the Internet to reach a large multimedia information such as video, images, and other kinds of news. As a multimedia data network news and public figures, mostly behavioral activity related to the behavior of public figures with the current activities are closely related to important events. Therefore, if we can easily picture the news public figures to search, then they can quickly understand the dynamics of various events. Face a lot of news images resources, if a relevant artificial looking image of the person, is a waste of manpower and time. If you can picture the characters in the news annotation and generate relational database, you can easily and quickly retrieve the characters. Take manual tagging method is effective, but multimedia and network technology, the rapid development makes growing image library, complete with hand-tagging, too much work. Network news images and text are interrelated, this use of the network news such illustrations, graphic complementary characteristics, generate some names marked face image databases. Get a lot of news on the web of data, the text portion of the data from the news and news related to extract names of the characters, and the text in the image corresponding news for face detection got a face image, and ultimately get a lot of with noise of the face names pairs. By learning can remove noise, to get the correct mapping of the human face names, images will appear in network news people use their names for automatic annotation. This will be the image retrieval problem into text retrieval problem which can solve this problem character retrieval. In this paper, based on Adaboost face detection algorithm to obtain a large number of images from the news of the face image by median filtering, histogram equalization to improve the image quality of the face, by the human eye positioning to correct and standardize the face image, get a normalized face image collections. For network news graphics complementary characteristics, the paper AP clustering algorithm is proposed to improve the AP clustering based on improved human face image annotation method, and ultimately get the face and the correspondence between the names. And propose a merger through the process of clustering to identify the same person in different names. AP improved clustering algorithm can take full advantage of the characteristics of the complementary graphic news, news of the data from the network for processing, and ultimately get accurate names marked with a face image database, and content-based searches based on character text character retrieval.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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