Deepthi p hudedagaddi

Bk tripathy

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Hybrid Intelligence for Image Analysis and Understanding

Data clustering has been an integral and important part of data mining. It has wide applications in database anonymization, decision making, image processing and pattern recognition, medical diagnosis, and geographical information systems, only to name a few. Data in real-life scenario are having imprecision inherent in them. So, early crisp clustering techniques are very less efficient. Several imprecisionbased models have been proposed over the years. Of late, it has been established that the hybrid models obtained as combination of these imprecise models are far more efficient than the individual ones. Several clustering algorithms have been put forth using these hybrid models. It is also found that conventional fuzzy clustering algorithms fail in incorporating the spatial information. This chapter focuses on discussing some of the spatial data clustering algorithms developed so far and their applications mainly in the area of image segmentation. © Springer International Publishing AG 2016.

Hybrid Soft Computing for Image Segmentation

Uncertainty-Based Spatial Data Clustering Algorithms for Image Segmentation

A fuzzy algorithm is presented for image segmentation of 2D gray scale images whose quality have been degraded by various kinds of noise. Traditional Fuzzy C Means (FCM) algorithm is very sensitive to noise and does not give good results. To overcome this problem, a new fuzzy c means algorithm was introduced [1] that incorporated spatial information. The spatial function is the sum of all the membership functions within the neighborhood of the pixel under consideration. The results showed that this approach was not as sensitive to noise as compared to the traditional FCM algorithm and yielded better results. The algorithm we have proposed adds an intuitionistic approach in the membership function of the existing spatial FCM (sFCM). Intuitionistic refers to the degree of hesitation that arises as a consequence of lack of information and knowledge. Proposed method is comparatively less hampered by noise and performs better than existing algorithms. © 2014 IEEE.

2014 IEEE International Conference on Computational Intelligence and Computing Research

Image segmentation using spatial intuitionistic fuzzy C means clustering

Several data clustering techniques have been developed in literature. It has been observed that the algorithms developed by using imprecise models like rough sets, fuzzy sets and intuitionistic fuzzy sets have been better than the crisp algorithms. Also, the hybrid models provide far better clustering algorithms than the individual models. Several such models have been developed by using a combination of fuzzy set introduced by Zadeh, the rough set introduced by Pawlak and the intuitionistic fuzzy introduced by Atanassov. Notable among them being the Rough Fuzzy C-Means (RFCM) introduced by Mitra et al and the rough intuitionistic fuzzy c-means algorithm (RIFCM) introduced and studied by Tripathy et al Krishnapuram and Keller observed that the basic clustering algorithms have the probabilistic flavour; for example due to the presence of the constraint on the memberships used in the fuzzy C-Means (FCM) algorithm. So, they introduced the concept of possibilistic approach and developed a possibilistic fuzzy C-means (PFCM) algorithm. Another approach to PFCM is due to Pal et al. In this paper, we improve a possibilistic rough C-Means (PRCM) algorithm introduced by Anuradha et, al. and introduce a new algorithm, which we call as possibilistic rough fuzzy C-Means (PRFCM) and compare its efficiency with the improved PRCM and the basic PRFCM algorithm to establish experimentally that this algorithm is comparatively better than PRCM and the corresponding RCM algorithm. We perform the experimental analysis by taking different types of numerical datasets and images as inputs and using standard accuracy measures like the DB and the D-index. © 2014 IEEE.

Possibilistic rough fuzzy C-means algorithm in data clustering and image segmentation

Clustering of real life data for analysis has gained popularity and imprecise methods or their hybrid approaches has attracted many researchers of late. Recently, rough intuitionistic fuzzy c-means algorithm was introduced and studied by Tripathy et al [3] and it was found to be superior to all other algorithms in this family. Kernel based counter part of these algorithms have been found to behave better than their corresponding Euclidean distance based algorithms. Very recently kernel based rough fuzzy algorithm was put forth by Bhargav et al [4]. A comparative analysis over standard datasets and images has established the superiority of this algorithm over its corresponding standard algorithm. In this paper we introduce the kernel based rough intuitionistic fuzzy c-means algorithm and show that it is superior to all the algorithms in the sequel; i.e. both normal and the kernel based algorithms. We establish it through experimental analysis by taking different type of inputs and using standard accuracy measures. © Springer International Publishing Switzerland 2014.

Smart Innovation, Systems and Technologies Advanced Computing, Networking and Informatics- Volume 1

On Kernel Based Rough Intuitionistic Fuzzy C-means Algorithm and a Comparative Analysis

Data clustering algorithms are used in many fields like anonymisation of databases, image processing, analysis of satellite images and medical data analysis. There are several C-Means clustering algorithms in the literature. Besides the hard C-Means, there are uncertainty based C-Means algorithms like the Fuzzy C-Means algorithm and its variants, the Rough C-Means algorithm, the Intuitionistic Fuzzy C- Means algorithm and the hybrid C-Means algorithms (Rough Fuzzy C-Means algorithm). In this paper we propose a new hybrid clustering algorithm called Rough Intuitionistic Fuzzy C-Means and evaluate its performance in comparison to the other algorithms mentioned above. We have applied these algorithms on numerical as well as image data of two different types and used some benchmarking indexes for the evaluation of their performance. The results show that the proposed algorithm outperforms the existing algorithms in almost all cases. © 2013 ACM.

Journal	Data powered by TypesetHybrid Intelligence for Image Analysis and Understanding
Publisher	Data powered by TypesetJohn Wiley & Sons, Ltd
Open Access	0