LS-SVM based image segmentation using color and texture information

Yang, Heng; Wang, Xiangyang; Wang, Qin-Yan; Zhang, Xian-Jin

doi:10.1016/j.jvcir.2012.07.007

Cited by 48 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The most common cases are from the perceptual models, where the and components [32][33][34] or the and components [35] or only the component [36,37] or a mixture between the components [38] is used.…”

Section: Problems With the Color Modelsmentioning

confidence: 99%

Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

Chavolla

Valdivia

Diaz

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Accurate color image segmentation has stayed as a relevant topic between the researches/scientific community due to the wide range of application areas such as medicine and agriculture. A major issue is the presence of illumination variations that obstruct precise segmentation. On the other hand, the machine learning unsupervised techniques have become attractive principally for the easy implementations. However, there is not an easy way to verify or ensure the accuracy of the unsupervised techniques; so these techniques could lead to an unknown result. This paper proposes an algorithm and a modification to the color model in order to improve the accuracy of the results obtained from the color segmentation using the -means++ algorithm. The proposal gives better segmentation and less erroneous color detections due to illumination conditions. This is achieved shifting the hue and rearranging the equation in order to avoid undefined conditions and increase robustness in the color model.

show abstract

Section: Problems With the Color Modelsmentioning

confidence: 99%

Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

Chavolla

Valdivia

Diaz

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In addition, some of those methods have been jointly used (e.g., neural networks 17,18 , genetic algorithm with multilevel thresholds 19 or some variant of support vector machines 20 ).…”

Section: Supervised Classificationmentioning

confidence: 99%

Classifying image analysis techniques from their output

Guada¹,

Gómez²,

Rodríguez³

et al. 2016

IJCIS

View full text Add to dashboard Cite

In this paper we discuss some main image processing techniques in order to propose a classification based upon the output these methods provide. Because despite a particular image analysis technique can be supervised or unsupervised, and can allow or not the existence of fuzzy information at some stage, each technique has been usually designed to focus on a specific objective, and their outputs are in fact different according to each objective. Thus, they are in fact different methods. But due to the essential relationship between them they are quite often confused. In particular, this paper pursues a clarification of the differences between image segmentation and edge detection, among other image processing techniques.

show abstract

“…Image segmentation techniques such as color texture based [55], coarse-to-fine strategy [56], wrapper based approach [57], content based image retrieval [58], dynamic region merging [59], Dual Multiscale Morphological Reconstructions and Retrieval Applications [37], background recognition and perceptual organization [60], niching particle swarm optimization [61], constraint satisfaction neural network [62], two stage self organizing network [63], adaptive local thresholds [64], vectorial scale-based fuzzyconnected image segmentation [65], mixed deterministic and Monte-Carlo [66], evaluation matrix based image segmentation [67], neutrosophic set and wavelet transformation [36], non linear distance matrix [68], shapeprior based image segmentation with intensity-based image registration [69] and least squares support vector machine (LS-SVM) [70] can be useful for object detection. In [71], survey of different image segmentation techniques is discussed.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…The authors focused on different unsupervised methods. Some of these methods such as [56], [36] and [70] can be applicable to number plate detection. The method discussed in [56] can be useful to detect multiple objects from the image.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Automatic Number Plate Recognition System (ANPR): A Survey

Patel¹,

Shah²,

Patel³

2013

IJCA

147

View full text Add to dashboard Cite

Traffic control and vehicle owner identification has become major problem in every country. Sometimes it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast. Therefore, it is not possible to catch and punish those kinds of people because the traffic personal might not be able to retrieve vehicle number from the moving vehicle because of the speed of the vehicle. Therefore, there is a need to develop Automatic Number Plate Recognition (ANPR) system as a one of the solutions to this problem. There are numerous ANPR systems available today. These systems are based on different methodologies but still it is really challenging task as some of the factors like high speed of vehicle, non-uniform vehicle number plate, language of vehicle number and different lighting conditions can affect a lot in the overall recognition rate. Most of the systems work under these limitations. In this paper, different approaches of ANPR are discussed by considering image size, success rate and processing time as parameters. Towards the end of this paper, an extension to ANPR is suggested.

show abstract

LS-SVM based image segmentation using color and texture information

Cited by 48 publications

References 32 publications

Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

Classifying image analysis techniques from their output

Automatic Number Plate Recognition System (ANPR): A Survey

Contact Info

Product

Resources

About