Color image segmentation using pixel wise support vector machine classification

Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a color image segmentation using pixel wise support vector machine (SVM) classification. Firstly, the pixel-level color feature and texture feature of the image, which is used as input of SVM model (classifier), are extracted via the local homogeneity model and Gabor filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.     

Color image segmentation using automatic pixel classification with support vector machine

Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM model (classifier). Then, the SVM model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.     

Adaptable image segmentation via simple pixel classification

We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well‐known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors; (ii) the approach suffers from very little overfitting and hence generalizes well to unseen images; (iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel‐centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion.     

Erroneous pixel prediction for semantic image segmentation

Computational Visual Media - We consider semantic image segmentation. Our method is inspired by Bayesian deep learning which improves image segmentation accuracy by modeling the uncertainty of the...     

基于像素聚类的超声图像分割

B型心脏超声图像分割是计算心功能参数前重要的一步。针对超声图像的低分辨率影响分割精度及基于模型的分割算法需要大样本训练集的问题,结合B型心脏超声图像的先验知识,提出了一种基于像素聚类进行图像分割的算法。首先,通过各向异性扩散处理图像;然后,使用一维K-均值对像素进行聚类;最后,根据聚类结果和先验知识将像素值修改为最佳类中心像素值。理论分析表明该算法可以使图像的峰值信噪比（PSNR）达到最大值。实验结果表明：所提算法比大津算法等更准确,PSNR较大津算法提高11.5%;即使在单张图像上也可以进行分割,且适应于分割任意形状的超声图像,有利于更准确地计算各种心功能参数。     

Color texture segmentation based on image pixel classification

Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Thus, image segmentation plays an important role in many multimedia applications. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. By combination of Fuzzy Support Vector Machine (FSVM) and Fuzzy C-Means (FCM), a color texture segmentation based on image pixel classification is proposed in this paper. Specifically, we first extract the pixel-level color feature and texture feature of the image via the local spatial similarity measure model and localized Fourier transform, which is used as input of FSVM model (classifier). We then train the FSVM model (classifier) by using FCM with the extracted pixel-level features. Color image segmentation can be then performed through the trained FSVM model (classifier). Compared with three other segmentation algorithms, the results show that the proposed algorithm is more effective in color image segmentation.     

Image segmentation and similarity of color-texture objects

We aim for content-based image retrieval of textured objects in natural scenes under varying illumination and viewing conditions. To achieve this, image retrieval is based on matching feature distributions derived from color invariant gradients. To cope with object cluttering, region-based texture segmentation is applied on the target images prior to the actual image retrieval process. The retrieval scheme is empirically verified on color images taken from textured objects under different lighting conditions.     

Unsupervised segmentation of color-texture regions in images andvideo

A method for unsupervised segmentation of color-texture regions in images and video is presented. This method, which we refer to as JSEG, consists of two independent steps: color quantization and spatial segmentation. In the first step, colors in the image are quantized to several representative classes that can be used to differentiate regions in the image. The image pixels are then replaced by their corresponding color class labels, thus forming a class-map of the image. The focus of this work is on spatial segmentation, where a criterion for “good” segmentation using the class-map is proposed. Applying the criterion to local windows in the class-map results in the “J-image,” in which high and low values correspond to possible boundaries and interiors of color-texture regions. A region growing method is then used to segment the image based on the multiscale J-images. A similar approach is applied to video sequences. An additional region tracking scheme is embedded into the region growing process to achieve consistent segmentation and tracking results, even for scenes with nonrigid object motion. Experiments show the robustness of the JSEG algorithm on real images and video     

An adaptive unsupervised approach toward pixel clustering and color image segmentation

This paper proposes an adaptive unsupervised scheme that could find diverse applications in pattern recognition as well as in computer vision, particularly in color image segmentation. The algorithm, named Ant Colony-Fuzzy C-means Hybrid Algorithm (AFHA), adaptively clusters image pixels viewed as three dimensional data pieces in the RGB color space. The Ant System (AS) algorithm is applied for intelligent initialization of cluster centroids, which endows clustering with adaptivity. Considering algorithmic efficiency, an ant subsampling step is performed to reduce computational complexity while keeping the clustering performance close to original one. Experimental results have demonstrated AFHA clustering's advantage of smaller distortion and more balanced cluster centroid distribution over FCM with random and uniform initialization. Quantitative comparisons with the X-means algorithm also show that AFHA makes a better pre-segmentation scheme over X-means. We further extend its application to natural image segmentation, taking into account the spatial information and conducting merging steps in the image space. Extensive tests were taken to examine the performance of the proposed scheme. Results indicate that compared with classical segmentation algorithms such as mean shift and normalized cut, our method could generate reasonably good or better image partitioning, which illustrates the method's practical value.     

基于测地线的超像素谱聚类彩色图像分割

在图像分割中谱聚类算法得到了广泛的应用,但传统谱聚类算法易受到彩色图像大小和相似性测度的影响,导致计算量大和分割精度低的问题。为了解决这两个问题,提出一种新的基于超像素集测地线特征的谱聚类分割算法。该方法通过对彩色图像进行预分割得到超像素集,并以超像素集为基础构造加权图,利用测地线距离特征和颜色特征构造权值矩阵,最后应用NJW（Ng-Jordan-Weiss）算法得到最终的分割结果。对比实验结果表明该算法在分割精度和计算复杂度上都有较大改善。     

多描述子活动轮廓模型的医学图像分割

目的 医学图像分割结果可帮助医生进行预测、诊断及制定治疗方案。医学图像在采集过程中受多种因素影响,同一组织往往具有不同灰度,且伴有强噪声。现有的针对医学图像的分割方法,对图像的灰度分布描述不够充分,不足以为精确的分割图像信息,且抗噪性较差。为实现医学图像的精确分割,提出一种多描述子的活动轮廓（MDAC）模型。方法 首先,引入图像的熵,结合图像的局部均值和方差共同描述图像的灰度分布。其次,在贝叶斯框架下,引入灰度偏移因子,建立活动轮廓模型的能量泛函。最后,利用梯度下降流法得到水平集演化公式,演化的最后在完成分割的同时实现偏移场的矫正。结果 利用合成图像和心脏、血管和脑等医学图像进行了仿真实验。利用MDAC模型对加噪的灰度不均图像进行分割,结果显示,在完成精确分割的同时实现了纠偏。通过对比分割前后图像的灰度直方图,纠偏图像只包含对应两相的两个峰,且界限更加清晰;与经典分割算法进行对比,MDAC在视觉效果和定量分析中,分割效果最好,比LIC的分割精度提高了30%多。结论 实验结果表明,利用均值、方差和局部熵共同描述图像灰度分布,保证了算法的精度。局部熵的引入,在保证算法精度的同时,提高了算法的抗噪性。能泛中嵌入偏移因子,保证算法精确分割的同时实现偏移场纠正,进一步提高分割精度。     

利用像素置换的自适应可逆信息隐藏

目的 像素置换作为一种可逆信息隐藏方式具有良好的抗灰度直方图隐写分析能力,但嵌入容量偏小一直是其缺陷。针对这一问题,提出了一种基于像素置换的自适应可逆信息隐藏算法。方法 首先,与传统2×2像素块结构相比构造了尺寸更小的像素对结构,使得载体图像可以被更稠密地分割,为嵌入容量的提升提供了基数条件。其次,提出适用于该新像素结构的可嵌像素对（EPP）筛选条件,避免嵌入过程引起图像质量大幅下降。之后,根据EPP的灰度趋势差异对其进行自适应预编码,提高Huffman编码压缩比,进一步提升算法嵌入容量。最终,通过像素置换嵌入信息。结果 与2×2像素块结构的非自适应图像隐写算法相比,在同样保证灰度直方图稳定性的情况下该算法的PSNR提高了32%左右,嵌入容量提高了95%以上。其中自适应性对嵌入容量提升的贡献极大。结论 本文算法同时具有抗灰度直方图隐写分析能力与高嵌入容量性的可逆信息隐藏。算法构造了更高效的可嵌单位,并且针对不同载体图像的特点对其可嵌区域进行差异化编码。实验结果表明,本文算法在具有更好的不可见性的同时,嵌入容量得到大幅提升。     

Skin segmentation using color pixel classification: analysis and comparison

This work presents a study of three important issues of the color pixel classification approach to skin segmentation: color representation, color quantization, and classification algorithm. Our analysis of several representative color spaces using the Bayesian classifier with the histogram technique shows that skin segmentation based on color pixel classification is largely unaffected by the choice of the color space. However, segmentation performance degrades when only chrominance channels are used in classification. Furthermore, we find that color quantization can be as low as 64 bins per channel, although higher histogram sizes give better segmentation performance. The Bayesian classifier with the histogram technique and the multilayer perceptron classifier are found to perform better compared to other tested classifiers, including three piecewise linear classifiers, three unimodal Gaussian classifiers, and a Gaussian mixture classifier.     

Optimized high speed pixel sorting and its application in watershed based image segmentation

Efficient sorting of image pixels based on their grayscale value is traditionally implemented using an algorithm based on distribution or counting sort methods. We show that an elegant alternative can be used which outperforms the traditional method both in terms of processing speed and main memory access. We discuss both theoretically analyzed and real-life performance results, and demonstrate the improvements that can be obtained when our algorithm is combined with a well-known watershed image segmentation method.     

Image segmentation by pixel classification

A fast boundary finding algorithm is presented which works without threshold operation and without any interactive control. The procedure can be described as a hierarchical two-step algorithm. In the first step the image is divided into two disjunct regions, one of them including the whole object of interest.In the second step the problem of boundary finding is suggested as a classification problem, which means that for any pixel a four-dimensional feature vector is computed which allows classification of pixels into contour elements and any other pixels.The algorithm was tested on several thousand cell images and can be easily adapted to other problems by modification of a set of parameters.