Does Image Segmentation Improve Object Categorization
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Does Image Segmentation Improve Object Categorization?
Image segmentation and object recognition are among the most fundamental problems in computer vision, and the potential interaction between these tasks has been discussed for many years. The usefulness of recognition for segmentation has been demonstrated with various top-down segmentation algorithms, however, the impact of bottom-up image segmentation as pre-processing for object recognition is not well understood. One factor impeding the utility of segmentation for recognition is the unsatisfactory quality of image segmentation algorithms. In this work we take advantage of a recently proposed method for computing multiple stable segmentations and illustrate the application of bottom-up image segmentation as a preprocessing step for object recognition and categorization. We extend a popular bag-of-features recognition model to provide multiple class categorization and localization of objects in images. We compare our categorization results to that of a conventional bag-of-features recognition model on the Caltech and PASCAL image databases.
Computer Vision - ECCV 2008
Author: David Forsyth
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-10-07
The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed proceedings of the 10th European Conference on Computer Vision, ECCV 2008, held in Marseille, France, in October 2008. The 243 revised papers presented were carefully reviewed and selected from a total of 871 papers submitted. The four books cover the entire range of current issues in computer vision. The papers are organized in topical sections on recognition, stereo, people and face recognition, object tracking, matching, learning and features, MRFs, segmentation, computational photography and active reconstruction.
Advances in Multimedia Information Processing -- PCM 2010, Part I
Author: Guoping Qiu
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-09-03
The 2010 Pacific-Rim Conference on Multimedia (PCM 2010) was held in Shanghai at Fudan University, during September 21–24, 2010. Since its inauguration in 2000, PCM has been held in various places around the Pacific Rim, namely Sydney (PCM 2000), Beijing (PCM 2001), Hsinchu (PCM 2002), Singapore (PCM 2003), Tokyo (PCM 2004), Jeju (PCM 2005), Zhejiang (PCM 2006), Hong Kong (PCM 2007), Tainan (PCM 2008), and Bangkok (PCM 2009). PCM is a major annual international conference organized as a forum for the dissemination of state-of-the-art technological advances and research results in the fields of theoretical, experimental, and applied multimedia analysis and processing. PCM 2010 featured a comprehensive technical program which included 75 oral and 56 poster presentations selected from 261 submissions from Australia, Canada, China, France, Germany, Hong Kong, India, Iran, Italy, Japan, Korea, Myanmar, Norway, Singapore, Taiwan, Thailand, the UK, and the USA. Three distinguished researchers, Prof. Zhi-Hua Zhou from Nanjing University, Dr. Yong Rui from Microsoft, and Dr. Tie-Yan Liu from Microsoft Research Asia delivered three keynote talks to the conference. We are very grateful to the many people who helped to make this conference a s- cess. We would like to especially thank Hong Lu for local organization, Qi Zhang for handling the publication of the proceedings, and Cheng Jin for looking after the c- ference website and publicity. We thank Fei Wu for organizing the special session on large-scale multimedia search in the social network settings.