K-means is undoubtedly themostwidely used partitional clustering algorithm. Unfortunately, due to the nonconvexity of the model formulations, expectation-maximization (EM) type algorithms converge to different local optima with different initializations. Recent discoveries have identified that the global solution of K-means cluster centroids lies in the principal component analysis (PCA) subspa...

Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method [4] in computer vision, our non-local operation computes the response at a position as a weighted sum of the fea...

Data clustering is a technique for clustering set of objects into known number of groups. Several approaches are widely applied to data clustering so that objects within the clusters are similar and objects in different clusters are far away from each other. K-Means, is one of the familiar center based clustering algorithms since implementation is very easy and fast convergence. However, K-Mean...

The unavoidable speckle in SAR images seriously interferes with image quality and has a negative effect on subsequent interpretation. existing filters still need to be strengthened terms of both noise suppression edge preservation. Based this, novel non-local means filter by multi-directional local plane inclination angle (MDLPIA) is proposed. proposed uses the MDLPIA reconstruct new weight fun...

One of the key issues in removing random-valued impulse noise from digital images using switching filters is the impulse noise detection. Impulse noise is a random, spiked variation in the brightness of the image. In this paper, a new impulse noise detection algorithm is presented that is based on Noise ratio Estimation and a combination of K-means clustering and Non-Local Means based filter (N...