Supervised color image segmentation, using LVQ networks and K-means. Application: cellular image

This paper proposes a new method for supervised color image classification by theKohonen map, based on LVQ algorithms. The sample of observations, constituted by image pixels with 3 color components in the color space, is at first projected into a Kohonen map. This map is represented in the 3-dimensional space, from the weight vectors resulting of the learning process . Image classification by kohonen is a low-level image processing task that aims at partitioning an image into homogeneous regions. How region homogeneity is defined depends on the application. In this paper color image quantisation by clustering is discussed. A clustering scheme, based on learning quantisation vector (LVQ), is constructed and compared to the K-means clustering algorithm. It is demonstrated that both perform equally well. However, the former performs better than the latter with respect to the known number of although class. Both depend on their initial conditions and may end up in local optima. Based on these findings, an LVQ scheme is constructed which is completely independent of initial conditions; this approach is a hybrid structure between competitive learning and splitting of the color space. For comparison, a K-means approach is applied; it is known to produce global optimal results, but with high computational load. The clustering scheme is shown to obtain near-global optimal results with low computational loadKeywords: color image, kohonen, LVQ, classification, K-mean

Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering

Incomplete multi-view clustering (IMVC) has received increasing attention since it is often that some views of samples are incomplete in reality. Most existing methods learn similarity subgraphs from original incomplete multi-view data and seek complete graphs by exploring the incomplete subgraphs of each view for spectral clustering. However, the graphs constructed on the original high-dimensional data may be suboptimal due to feature redundancy and noise. Besides, previous methods generally ignored the graph noise caused by the inter-class and intra-class structure variation during the transformation of incomplete graphs and complete graphs. To address these problems, we propose a novel Joint Projection Learning and Tensor Decomposition Based method (JPLTD) for IMVC. Specifically, to alleviate the influence of redundant features and noise in high-dimensional data, JPLTD introduces an orthogonal projection matrix to project the high-dimensional features into a lower-dimensional space for compact feature learning.Meanwhile, based on the lower-dimensional space, the similarity graphs corresponding to instances of different views are learned, and JPLTD stacks these graphs into a third-order low-rank tensor to explore the high-order correlations across different views. We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition based graph filter for robust clustering.JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor. The intrinsic tensor models the true data similarities. An effective optimization algorithm is adopted to solve the JPLTD model. Comprehensive experiments on several benchmark datasets demonstrate that JPLTD outperforms the state-of-the-art methods. The code of JPLTD is available at https://github.com/weilvNJU/JPLTD.Comment: IEEE Transactions on Neural Networks and Learning Systems, 202