Fast Pixel Classification by SVM Using Vector Quantization, Tabu Search and Hybrid Color Space

Lebrun, Gilles; Charrier, Christophe; Lézoray, Olivier; Meurie, Cyril; Cardot, Hubert

doi:10.1007/11556121_84

Cited by 13 publications

(17 citation statements)

References 8 publications

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“…Adults and Shuttle come from UCI repository [18], Web from [1] and ClassPixels from [7]. Learning and test sets contain respectively 2/3 and 1/3 of initial datasets.…”

Section: Resultsmentioning

confidence: 99%

“…With our model selection method, pixel classification ( fig. 2) can be performed with only 7 SVs and 4 color features (see [7] for further details).…”

Section: Resultsmentioning

confidence: 99%

“…One way is to optimize the SVM algorithm [1,4] to solve the associated quadratic problem. Other approaches use a simplification step to reduce the training set size [2,3,5,6,7]. For learning methods using SVM, model selection is critical.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Model Selection Method for SVM

Lebrun¹,

Lézoray²,

Charrier³

et al. 2006

Intelligent Data Engineering and Automated Learning – IDEAL 2006

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Abstract. In this paper, a new learning method is proposed to build Support Vector Machines (SVMs) Binary Decision Functions (BDF) of reduced complexity and efficient generalization. The aim is to build a fast and efficient SVM classifier. A criterion is defined to evaluate the Decision Function Quality (DFQ) which blendes recognition rate and complexity of a BDF. Vector Quantization (VQ) is used to simplify the training set. A model selection based on the selection of the simplification level, of a feature subset and of SVM hyperparameters is performed to optimize the DFQ. Search space for selecting the best model being huge, Tabu Search (TS) is used to find a good sub-optimal model on tractable times. Experimental results show the efficiency of the method.

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“…Adults and Shuttle come from UCI repository [18], Web from [1] and ClassPixels from [7]. Learning and test sets contain respectively 2/3 and 1/3 of initial datasets.…”

Section: Resultsmentioning

confidence: 99%

“…With our model selection method, pixel classification ( fig. 2) can be performed with only 7 SVs and 4 color features (see [7] for further details).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A New Model Selection Method for SVM

Lebrun¹,

Lézoray²,

Charrier³

et al. 2006

Intelligent Data Engineering and Automated Learning – IDEAL 2006

Self Cite

View full text Add to dashboard Cite

show abstract

“…From all existing classification schemes, a Support Vector Machine (SVM)-based technique has been selected due to high classification rates obtained in previous works, 18 and to their high generalization abilities.…”

Section: Classification Methodsmentioning

confidence: 99%

Image quality assessment with manifold and machine learning

2009

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A crucial step in image compression is the evaluation of its performance, and more precisely the available way to measure the final quality of the compressed image. In this paper, a machine learning expert, providing a final class number is designed. The quality measure is based on a learned classification process in order to respect the one of human observers. Instead of computing a final note, our method classifies the quality using the quality scale recommended by the UIT. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). This was done constructing a vector containing many visual attributes. Finally, the final features vector contains more than 40 attibutes.Unfortunatley, no study about the existing interactions between the used visual attributes has been done. A feature selection algorithm could be interesting but the selection is highly related to the further used classifier. Therefore, we prefer to perform dimensionality reduction instead of feature selection. Manifold Learning methods are used to provide a low-dimensional new representation from the initial high dimensional feature space.The classification process is performed on this new low-dimensional representation of the images. Obtained results are compared to the one obtained without applying the dimension reduction process to judge the efficiency of the method.

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“…From all existing classification schemes, a SVM-based technique has been selected due to high classification rates obtained in previous works [20] and to their high generalization abilities. The SVMs were developed by Vapnik [17] and are based on the structural risk minimization principle from statistical learning theory.…”

Section: Multi-class Svm Classificationmentioning

confidence: 99%

A SVM-based model for the evaluation of biometric sample quality

El-Abed

Giot

Hemery

et al. 2011

2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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Abstract-One of the main factors affecting the performance of biometric systems is the quality of the acquired samples. Poorquality samples increase the enrollment failure, and decrease the system performance. Therefore, it is important for a biometric system to estimate the quality of the acquired biometric samples. Toward this goal, we present in this paper a multi-class SVMbased method to predict sample quality. The proposed method uses two types of information: the first one is based on the image quality and the second is a pattern-based quality using the SIFT keypoints extracted from the image. For the experiments, we use four large and significant face databases to show the efficiency of the proposed method in predicting the system performance illustrated by the Equal Error Rate (EER).

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Fast Pixel Classification by SVM Using Vector Quantization, Tabu Search and Hybrid Color Space

Cited by 13 publications

References 8 publications

A New Model Selection Method for SVM

A New Model Selection Method for SVM

Image quality assessment with manifold and machine learning

A SVM-based model for the evaluation of biometric sample quality

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