東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Object and concept recognition for c...

Li, Yi.

FindBook

Google Book

Amazon

博客來

Object and concept recognition for content-based image retrieval.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Object and concept recognition for content-based image retrieval./
作者:	Li, Yi.
面頁冊數:	88 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 0990.
Contained By:	Dissertation Abstracts International66-02B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3163394
ISBN:	0496976508

Object and concept recognition for content-based image retrieval.
Li, Yi.

Object and concept recognition for content-based image retrieval. - 88 p.

Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 0990.

Thesis (Ph.D.)--University of Washington, 2005.

The problem of recognizing classes of objects in images is important for annotation and indexing of image and video databases. Users of commercial CBIR systems prefer to pose their queries in terms of key words. To help automate the indexing process, we represent images as sets of feature vectors of multiple types of abstract regions, which come from various segmentation processes. With this representation, we have developed two new algorithms to recognize classes of objects and concepts in outdoor photographic scenes. The semi-supervised EM-variant algorithm models each abstract region as a mixture of Gaussian distributions over its feature space. The more powerful generative/discriminative learning algorithm is a two-phase method. The generative phase normalizes the description length of images, which can have an arbitrary number of extracted features. In the discriminative phase, a classifier learns which images, as represented by this fixed-length description, contain the target object. We have tested our approaches by experimenting with several different data sets and combinations of features. Our results showed a significant improvement over the published results.

ISBN: 0496976508Subjects--Topical Terms:

626642
Computer Science.

Object and concept recognition for content-based image retrieval.
LDR:02043nmm 2200265 4500 001 1849460
005 20051205073952.5
008 130614s2005 eng d
020 $a 0496976508
035 $a (UnM)AAI3163394
035 $a AAI3163394
040 $a UnM $c UnM
100 1 $a Li, Yi. $3 911053
245 1 0 $a Object and concept recognition for content-based image retrieval.
300 $a 88 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 0990.
500 $a Chair: Linda G. Shapiro.
502 $a Thesis (Ph.D.)--University of Washington, 2005.
520 $a The problem of recognizing classes of objects in images is important for annotation and indexing of image and video databases. Users of commercial CBIR systems prefer to pose their queries in terms of key words. To help automate the indexing process, we represent images as sets of feature vectors of multiple types of abstract regions, which come from various segmentation processes. With this representation, we have developed two new algorithms to recognize classes of objects and concepts in outdoor photographic scenes. The semi-supervised EM-variant algorithm models each abstract region as a mixture of Gaussian distributions over its feature space. The more powerful generative/discriminative learning algorithm is a two-phase method. The generative phase normalizes the description length of images, which can have an arbitrary number of extracted features. In the discriminative phase, a classifier learns which images, as represented by this fixed-length description, contain the target object. We have tested our approaches by experimenting with several different data sets and combinations of features. Our results showed a significant improvement over the published results.
590 $a School code: 0250.
650 4 $a Computer Science. $3 626642
690 $a 0984
710 2 0 $a University of Washington. $3 545923
773 0 $t Dissertation Abstracts International $g 66-02B.
790 1 0 $a Shapiro, Linda G., $e advisor
790 $a 0250
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3163394