Multilevel thresholding is a simple and effective method in numerous image segmentation
applications. In this paper, we propose a new multilevel thresholding method that uses …

In this work, a multilevel thresholding approach that uses modified bacterial foraging
optimization (MBFO) is presented for enhancing the applicability and practicality of optimal …

The performance of subspace clustering is affected by data representation. Data
representation for subspace clustering maps data from the original space into another space …

In order to solve the problem of high false positive in network intrusion detection systems, we
adopted clustering algorithms, the K-means algorithm and the Fuzzy C Mean (FCM) …

In this paper, a new Fuzzy Learning Vector Quantization (FuzzLVQ) method for classification
is presented. FuzzLVQ is a hybrid method based on LVQ neural networks and fuzzy …

To improve the effectiveness and robustness of the existing semisupervised fuzzy clustering
for segmenting image corrupted by noise, a kernel space semisupervised fuzzy C‐means …

The object present in an image is an important content and can be used in CBIR
applications. Identifying and representing the shape of the object is indeed complex due to …

Data stream mining refers to methods able to mine continuously arriving and evolving data
sequences or even large scale static databases. Mining data streams has attracted much …

Image segmentation refers to the technology to segment the image into different regions with
different characteristics and to extract useful objectives, and it is a key step from image …

Annotation of shapes is an important process for semantic image retrieval. In this paper, we
present a shape annotation framework that enables intelligent image retrieval by exploiting …