color-snake

基于图像检索的matlab代码，基于内容的图像检索中的一些关键环节：特征提取：颜色直方图；纹理特征等 相似度：马氏距离，欧氏距离等 相关反馈：机器学习方法，如SVM，神经网络等 检索与分类：两个很相似的样本距离很小，虽然两个不相似的样本距离未必很大(content-based image retrieval of some of the key issues : Feature Extraction : color histogram Texture characteristics of similarity : Mahalanobis distance, the Euclidean distance relevance feedback : machine learning methods, such as SVM. Neural network search and classification : two very similar samples from the small, although the two are similar to the samples may not be much distance)

BEMD对图像进行分解 Detailed two-dimensional empirical mode decomposition (BEMD decomposition of the image)

加入乘性噪声和椒盐噪声的图像进行维纳滤波和均值滤波，采用不同窗口看效果(Add the image of multiplicative noise and salt and pepper noise for Wiener filtering and mean filtering, using different windows to see the effect)

用matlab实现两幅彩色图像的无缝融合，输出结果为灰度图象(Using two color images matlab realize seamless integration, the output for gray-scale image)

分别用一阶、二阶以及Canny边缘检测算法进行图像边缘检测(Respectively first, second, and Canny edge detection algorithm for image edge detection)

三步搜索法运动估计，用于视频处理的学习和论文(Three-step search motion estimation for video processing of the study and thesis)

DIX公式优化求解层速度，使得速度变得更加平滑。(DIX formula optimization solution velocity, making speed becomes smoother.)

基于最大类间方差准则的阈值分割算法。其基本思想是对像素进行划分，通过划分得到的各类之间的距离达到最大，来确定合适的门限。(Based on the maximum between-cluster variance criteria threshold segmentation algorithm. The basic idea is to divide pixels, obtained through the division of the distance between the various types of maximum, to determine the appropriate threshold.)

说明：  该算法的功能是实现时频分析中多种分析方法后续处理中的图像压缩功能，进一步提高图像的时频分辨率(The function of the algorithm is to realize the image compression function in the subsequent processing of various analysis methods in time-frequency analysis, and further improve the time-frequency resolution of the image)

主成分分析方法（PCA），PCA算法的理论依据是K-L变换，通过一定的性能目标来寻找线性变换W，实现对高维数据的降维。(Principal component analysis (PCA), PCA algorithm is based on the theory of KL transform, through a certain performance targets to find the linear transformation W, the realization of high-dimensional data, dimensionality reduction.)