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Scene and Video Understanding.

Jain, Arpit.

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Scene and Video Understanding.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Scene and Video Understanding./
Author:	Jain, Arpit.
Description:	116 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
Contained By:	Dissertation Abstracts International75-11B(E).
Subject:	Computer Science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3628526
ISBN:	9781321045277

Scene and Video Understanding.
Jain, Arpit.

Scene and Video Understanding. - 116 p.

Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2014.

There have been significant improvements in the accuracy of scene understanding due to a shift from recognizing objects "in isolation" to context based recognition systems. Such systems improve recognition rates by augmenting appearance based models of individual objects with contextual information based on pairwise relationships between objects. These pairwise relations incorporate common sense world knowledge such as co-occurrences and spatial arrangements of objects, temporal consistency, scene layout, etc. However, these relations, even though consistent in the 3D world, change due to viewpoint of the scene. In this thesis, we investigate incorporating contextual information from three different perspectives for scene and video understanding (a) "what" contextual relations are useful and "how" they should be incorporated into Markov network during inference, (b) jointly solving the segmentation and recognition problem using a multiple segmentation framework based on contextual information in conjunction with appearance matching, and (c) proposing a discriminative spatio-temporal patch based representation for videos which incorporates contextual information for video understanding.

ISBN: 9781321045277Subjects--Topical Terms:

626642
Computer Science.

Scene and Video Understanding.
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245 1 0 $a Scene and Video Understanding.
300 $a 116 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-11(E), Section: B.
500 $a Adviser: Larry S. Davis.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2014.
520 $a There have been significant improvements in the accuracy of scene understanding due to a shift from recognizing objects "in isolation" to context based recognition systems. Such systems improve recognition rates by augmenting appearance based models of individual objects with contextual information based on pairwise relationships between objects. These pairwise relations incorporate common sense world knowledge such as co-occurrences and spatial arrangements of objects, temporal consistency, scene layout, etc. However, these relations, even though consistent in the 3D world, change due to viewpoint of the scene. In this thesis, we investigate incorporating contextual information from three different perspectives for scene and video understanding (a) "what" contextual relations are useful and "how" they should be incorporated into Markov network during inference, (b) jointly solving the segmentation and recognition problem using a multiple segmentation framework based on contextual information in conjunction with appearance matching, and (c) proposing a discriminative spatio-temporal patch based representation for videos which incorporates contextual information for video understanding.
520 $a Our work departs from traditional view of incorporating context into scene understanding where a fixed model for context is learned. We argue that context is scene dependent and propose a data-driven approach to predict the importance of relationships and construct a Markov network for image analysis based on statistical models of global and local image features. Since all contextual information is not equally important, we also address the related problem of predicting the feature weights associated with each edge of a Markov network for evaluation of context. We then address the problem of fixed segmentation while modeling context by using a multiple segmentation framework and formulating the problem as "a jigsaw puzzle". We formulate the labeling problem as segment selection from a pool of segments (jigsaws), assigning each selected segment a class label. Previous multiple segmentation approaches used local appearance matching to select segments in a greedy manner. In contrast, our approach is based on a cost function that combines contextual information with appearance matching. A relaxed form of the cost function is minimized using an efficient quadratic programming solver.
520 $a Lastly, we propose a new representation for videos based on mid-level discriminative spatio-temporal patches. These patches might correspond to a primitive human action, a semantic object, or perhaps a random but informative spatiotemporal patch in the video. What define these spatiotemporal patches are their discriminative and representative properties. We automatically mine these patches from hundreds of training videos and experimentally demonstrate that these patches establish correspondence across videos. We propose a cost function that incorporates co-occurrence statistics and temporal context along with appearance matching to select subset of these patches for label transfer. Furthermore, these patches can be used as a discriminative vocabulary for action classification.
590 $a School code: 0117.
650 4 $a Computer Science. $3 626642
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Engineering, Computer. $3 1669061
690 $a 0984
690 $a 0544
690 $a 0464
710 2 $a University of Maryland, College Park. $b Electrical Engineering. $3 1018746
773 0 $t Dissertation Abstracts International $g 75-11B(E).
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791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3628526