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

Multimodal multimedia metadata fusion.

Wu, Yi.

FindBook

Google Book

Amazon

博客來

Multimodal multimedia metadata fusion.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multimodal multimedia metadata fusion./
作者:	Wu, Yi.
面頁冊數:	190 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4331.
Contained By:	Dissertation Abstracts International66-08B.
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3186812
ISBN:	9780542280528

Multimodal multimedia metadata fusion.
Wu, Yi.

Multimodal multimedia metadata fusion. - 190 p.

Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4331.

Thesis (Ph.D.)--University of California, Santa Barbara, 2005.

The past decade has witnessed a phenomenal growth in both the volume and the variety of information. From a mostly textual world, we now see the widespread use of multimedia data such as videos, images, bio-sequences, etc. Such changes have led to increasing demands for improved data mining techniques to cope with the added complexity. In this dissertation, we focus on information fusion techniques for mapping multimedia data to semantics.

ISBN: 9780542280528Subjects--Topical Terms:

626642
Computer Science.

Multimodal multimedia metadata fusion.
LDR:03461nmm 2200325 4500 001 1820562
005 20061114130250.5
008 130610s2005 eng d
020 $a 9780542280528
035 $a (UnM)AAI3186812
035 $a AAI3186812
040 $a UnM $c UnM
100 1 $a Wu, Yi. $3 1672879
245 1 0 $a Multimodal multimedia metadata fusion.
300 $a 190 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-08, Section: B, page: 4331.
500 $a Chair: Edward Y. Chang.
502 $a Thesis (Ph.D.)--University of California, Santa Barbara, 2005.
520 $a The past decade has witnessed a phenomenal growth in both the volume and the variety of information. From a mostly textual world, we now see the widespread use of multimedia data such as videos, images, bio-sequences, etc. Such changes have led to increasing demands for improved data mining techniques to cope with the added complexity. In this dissertation, we focus on information fusion techniques for mapping multimedia data to semantics.
520 $a Multimedia data carry multimodal information in the forms of semantics, context, and content, of which content can consist of visual, audio and textual information. Our work first extracts multimodal features and identifies individual modalities. Once modalities have been identified, we quantify similarity measures for each modality. Many distance measures for multimedia data are non-metric in nature (resulting in non-positive-definite similarity matrices). Kernel machines, with their spectacular results on diverse datasets, however, work only with positive semi-definite matrices. We employ the approach of spectrum transformation to generate a positive semi-definite kernel matrix. Once the individual modalities have been identified and individual distance measures have been designed, we use super-kernel fusion and Bayesian inference learning to fuse multiple modalities in a query-dependent way.
520 $a We also studied two important applications for multimodal multimedia data fusion: video event recognition and video structure analysis. Detecting hazardous events from videos has spurred new research for security concerns. We present a framework for multi-camera video surveillance, which consists of three phases: detection, representation, and recognition. The detection phase handles spatio-temporal data fusion from multiple cameras for extracting motion data. The representation phase constructs content-rich descriptions of the motion events. The recognition phase deals with suspicious event identification based on the data descriptors.
520 $a Detecting video shot boundaries provides the ground for nearly all existing video analysis and segmentation algorithms. A shot transition takes place where inter-frame difference is perceptually significant. We use the dynamic partial function as the inter-frame difference measurement to detect perceptual discontinuity, and hence the boundary of a shot.
520 $a Through theoretical analysis and extensive empirical studies, we show that our proposed approaches are able to perform more effectively, and efficiently, than traditional methods.
590 $a School code: 0035.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Electronics and Electrical. $3 626636
690 $a 0984
690 $a 0544
710 2 0 $a University of California, Santa Barbara. $3 1017586
773 0 $t Dissertation Abstracts International $g 66-08B.
790 1 0 $a Chang, Edward Y., $e advisor
790 $a 0035
791 $a Ph.D.
792 $a 2005
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3186812