Heterogeneous Feature Fusion for Visual Recognition

In the past decade, the popularity of the Internet and digital cameras has led to a flourishing of images and videos. Surveillance videos are increasing explosively with the huge amounts of surveillance cameras. Compared with traditional datasets in computer vision, which host only thousands of images, these large-scale datasets in the era of the Internet have grown beyond the wildest imagination, and posed a serious challenge for visual recognition and detection. To handle the challenge of visual recognition in complicated scenarios, we believe that a single feature is not enough to distinguish web-scale visual concepts. Accordingly, this dissertation proposes to combine heterogeneous features for different visual recognition tasks. We first develop a machinery called Heterogeneous Feature Machines to effectively fuse multiple types of visual features. In addition, we realize that in specific applications such as consumer photo annotation or surveillance action detection, there are also specific cues which are helpful for visual recognition tasks. We consider three scenarios: (1) consumer photo recognition, where we explore the use of metadata such as time and GPS, (2) Web image searching and annotation, where we combine both user tags and network information for visual applications, and (3) action detection in videos, where the spatial-temporal coherence is combined with multiple visual features for detection tasks. We believe heterogeneous feature fusion is useful in a wide range of applications and merits research efforts in this promising direction.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Cao, Liangliang

Contributors dc:contributor

• Huang, Thomas S.
• Han, Jiawei
• Luo, Jiebo
• Ahuja, Narendra
• Hasegawa-Johnson, Mark A.

• action detection
• geophoto memex
• visual recognition
• heterogeneous feature machines

Statement dc:rights

• Copyright 2011 Liangliang Cao

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/26213

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/26213