Theme-Based Multi-class Object Recognition and Segmentation

In this paper, we propose a new theme-based CRF model and investigate its performance on class based pixel-wise segmentation of images. By including the theme of an image, we also propose a new texture-environment potential to represent texture environment of a pixel, which alone gives satisfactory...

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Bibliographic Details
Published in:2010 20th International Conference on Pattern Recognition pp. 3013 - 3016
Main Authors: Shilin Wu, Jiajia Geng, Feng Zhu
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2010
Subjects:
Accuracy
Conditional random field(CRF)
Image recognition
Image segmentation
Joint-boost
Object recognition
Pixel
Training
Visualization
ISBN:1424475422, 9781424475421
ISSN:1051-4651
Online Access:Get full text
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Summary:In this paper, we propose a new theme-based CRF model and investigate its performance on class based pixel-wise segmentation of images. By including the theme of an image, we also propose a new texture-environment potential to represent texture environment of a pixel, which alone gives satisfactory recognition results. The pixel-wise segmentation accuracy is remarkably improved by introducing texture potential. We compare our results to recent published results on the MSRC 21-class database and show that our theme-based CRF model significantly outperforms the current state-of-the-art. Especially, by assigning a theme for each image, our model obtains greatly improved accuracy of structured classes with high visual variability and fewer training examples, the accuracy of which is very low in most related works.
ISBN:1424475422
9781424475421
ISSN:1051-4651
DOI:10.1109/ICPR.2010.738