Color image segmentation using pixel wise support vector machine classification

Wang, Xiangyang; Wang, Ting; Bu, Juan

doi:10.1016/j.patcog.2010.08.008

Cited by 159 publications

(95 citation statements)

References 20 publications

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“…It has been used in various applications such as text classification [5], facial expression recognition [9], gene analysis [4] and many others [1,6,7,8,10,11,12,17,19,20,21,22]. Recently, Wang et al [15] presented SVM based fault classifier design for a water level control system.…”

Section: Support Vector Machinementioning

confidence: 99%

Data classification with support vector machine and generalized support vector machine

Qi¹,

Silvestrov

Nazır

2017

AIP Conference Proceedings

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In this paper, we study the support vector machine and introduced the notion of generalized support vector machine for classification of data. We show that the problem of generalized support vector machine is equivalent to the problem of generalized variational inequality and establish various results for the existence of solutions. Moreover, we provide various examples to support our results.---------------

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Section: Support Vector Machinementioning

confidence: 99%

Data classification with support vector machine and generalized support vector machine

Qi¹,

Silvestrov

Nazır

2017

AIP Conference Proceedings

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“…In color images, texture is usually extracted either in the intensity image component [47][48][49][50] or in each of the three RGB bands separately [51][52][53]. Both of the two strategies were tested in two different classifications.…”

Section: Textural Extraction and Classificationmentioning

confidence: 99%

New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

Safia

2013

ISRN Machine Vision

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Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.

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“…Today the field of machine learning and pattern recognition finds applications not only in the traditional fields like speech recognition [29,67] but also in new and emerging research areas (for example, isolated word recognition [23] using lip reading). Machine learning has also been used for many image processing applications such as image classification [56]; image segmentation [3,52], which is often used in many video and computer vision applications such as object localization/tracking/recognition, signal compression, and image retrieval [47]; image watermarking [54]; handwriting recognition [9]; age estimation from facial images [17]; object detection [59]; sketch recognition [69]; texture classification [75], etc. We refer the reader to [43,80] for comprehensive reviews of the applications of machine learning in image processing.…”

Section: Signal Processing Based Approach For Iqamentioning

confidence: 99%

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Narwaria

Wang

Çetin

2012

Pattern Recognition

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a b s t r a c tMeasurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/ parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics.

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Color image segmentation using pixel wise support vector machine classification

Cited by 159 publications

References 20 publications

Data classification with support vector machine and generalized support vector machine

Data classification with support vector machine and generalized support vector machine

New Brodatz-Based Image Databases for Grayscale Color and Multiband Texture Analysis

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

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