Year: 2011
Type: article
Abstract: 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 explosive-ly 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 imagi-nation, and posed a serious challenge for visual recognition and detection. To handle the challenge of visual recognition in complicated scenarios, we that a sin-gle feature is not enough to distinguish web-scale visual concepts. Accordingly, this dissertation proposes to combine heterogeneous features for different visu-al recognition tasks. We first develop a machinery called Heterogeneous Feature Machines to effectively fuse multiple types of visual features. In addition, we real-ize that in specific applications such as consumer photo annotation or surveillance action detection, there are also specific cues which are helpful for visual recogni-tion 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 be-lieve heterogeneous feature fusion is useful in a wide range of applications and merits research efforts in this promising direction. ii To my wife Tanya iii ACKNOWLEDGMENTS First I want thank my incredible adviser, Prof. Thomas S. Huang, for all the patience, advice and guidance which shaped me into a UIUC PhD and a researcher for future life. I am also very lucky to have had the chance to work with Dr. Jiebo
Author: Liangliang Cao
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2249511963