Proceedings Article10.1109/ALPIT.2007.28
A Convolution Kernel Method for Color Recognition
Jeong-Woo Son,Seong-Bae Park,Ku-Jin Kim +2 more
- 01 Aug 2007
- pp 242-247
20
TL;DR: A novel convolution kernel method to extract color information from out-door images by mapping images onto a high-dimentional feature space of which features are image fragments of two images and then the similarity between them is obtained through the inner-production of two image vectors.
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Abstract: Color recognition for out-door images is important for low-level computer vision, but it is a difficult task due to the effect of circumstances such as illumination, weather and so on. In this paper, we propose a novel convolution kernel method to extract color information from out-door images.When two images are compared, the proposed kernel maps images onto a high-dimentional feature space of which features are image fragments of two images and then the similarity between them is obtained through the inner-production of two image vectors. To evaluate the proposed kernel, it is applied to the vehicle color recognition problem. In the experiments on 500 vehicle images, the vehicle color recognition model with the proposed kernel shows about 92% of precision and 92% of recall. On the other hands, the model with a linear kernel shows about 45% of precision and 45% of recall. These experimental results imply that the proposed kernel is a plausible approach for the color recognition task.
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Citations
Pattern Recognition and Machine Learning
Christopher M. Bishop
- 01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
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Vehicle Color Recognition on Urban Road by Feature Context
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TL;DR: A method to implicitly select the ROI for color recognition in vehicle images by assigning the subregions with different weights that are learned by a classifier trained on the vehicle images is proposed.
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Vehicle Color Recognition using Convolutional Neural Network
TL;DR: It is proved that CNN can also learn classification based on color distribution, and the model outperform the original system provide by Chen with 2% higher overall accuracy.
A vehicle color classification method for video surveillance system concerning model-based background subtraction
Yi-Ta Wu,Jau-Hong Kao,Ming-Yu Shih +2 more
- 21 Sep 2010
TL;DR: A novel VCC algorithm based on refining the foreground mask with following two steps is presented to ensure classification result, which found the proposed approach is promising to automatic determination of vehicle colors in a video surveillance system.
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A view-invariant and anti-reflection algorithm for car body extraction and color classification
Hui-Zhen Gu,Suh-Yin Lee +1 more
TL;DR: The experimental results show that the tri-state method can extract almost 90% of car body pixels from a car image, and the average accuracy of the 10-color-type classification is higher than 93%.
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References
Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
30.8K
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Pattern Recognition and Machine Learning
Christopher M. Bishop
- 17 Aug 2006
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Pattern Recognition and Machine Learning
Christopher M. Bishop
- 01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
10.1K
Nonlinear component analysis as a kernel eigenvalue problem
TL;DR: A new method for performing a nonlinear form of principal component analysis by the use of integral operator kernel functions is proposed and experimental results on polynomial feature extraction for pattern recognition are presented.