Sweeper's Algorithm and its Application on Image Clustering
نویسندگان
چکیده
منابع مشابه
A Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...
متن کاملClustering Hierarchical Fuzzy Colors and its Application on Image segmentation
The purpose of this research is to analyze the lowlevel features of images as a representation of semantic concept. This paper proposes a flexible approach for clustering features of images and mapping the low-level image features to the highlevel concept recognition. Owing to the uncertainty of image features for human’s recognition, we use the approach of fuzzy colors clustering to analyze im...
متن کاملA Novel Clustering Algorithm Based on Bayesian Sequential Partition and Its Application in Image Segmentation
In this work, we propose a novel clustering algorithm based on Bayesian Sequential Partition (BSP) and the spectral clustering algorithm. Since the BSP is capable of providing much more accurate density estimates when the sample space is of moderate to high dimension, the proposed clustering algorithm is believed to be superior to available ones when dealing with high dimensional problems. To d...
متن کاملA Novel Weighted Semi-Supervised Clustering Algorithm and its Application in Image Segmentation
In this paper we propose a novel weighted semi-supervised clustering algorithm and then study on how to apply it in the problem of image segmentation. We explain how to obtain weights of the semi-supervised clustering algorithm using the number of unlabeled data samples and the number of data samples. After defining the data sample weights, the next task is to obtain the cluster labels by optim...
متن کاملMinimum-Entropy Clustering and its Application to Lossless Image Coding
The Minimum-Entropy Clustering (MEC) algorithm proposed in this paper provides an optimal method for addressing the non-stationarity of a source with respect to entropy coding. This algorithm clusters a set of vectors (where each vector consists of a xed number of contiguous samples from a discrete source) using a minimum entropy criterion. In a manner similar to Classi ed Vector Quantization (...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computer Applications
سال: 2015
ISSN: 0975-8887
DOI: 10.5120/21090-3785