Informatics Publishing Limited

K. S. LakshmiPriya ¹, L. Sankari ²

Affiliations
1 Sri Ramakrishna CAS for Women, Coimbatore-44, IN
2 Department of Computer Science, Sri Ramakrishna CAS for Women, Coimbatore-44, IN

Abstract

In Recent days Semi Supervised Clustering plays a noteworthy role in image processing which helps the image segmentation to produce the efficient result of an input image. Semi Supervised clustering which is combinational of both labeled and unlabeled data points, typically with the large amount of unlabeled data and a small amount of labeled data. Semi supervised clustering falls between unsupervised (without any labeled training data) and supervised (with completely labeled training data). It means that a small amount of human assistance or prior information is given during clustering process. This paper is focuses on survey of Semi Supervised clustering techniques for image segmentation.

Keywords

Semi Supervised Clustering, Labeled Data, Unlabeled Data.

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L. Sankari ¹, C. Chandrasekar ²

Affiliations
1 Department of comp.Science, Sri Ramakrishna College of Arts and Science for women, Coimbatore, IN
2 Dept of Computer Science, Periyar University, Salem, IN

Abstract

In image processing, segmentation means dividing the image into homogeneous regions. The task of recognizing the patterns is a crucial task. Since the result is based on which image segmentation algorithm the application is using. A number of image segmentation algorithms are available using data mining techniques like clustering based algorithms, classification based algorithms and semi supervised based algorithms. This paper discusses about two semi supervised image segmentation ideas with one standard model based algorithm (EM Cluster Algorithm). In semi supervised method both labeled and unlabeled data are used to improve the performance of segmentation. The first paper discuss about standard EM algorithms The second paper discuss about semi supervised image segmentation using mouse clicks as prior information and the third paper discuss about optimal seed selection with semi supervised segmentation. The result of analysis shows that the optimal seed selection (method III) gives better results and then clustering gives more accurate results. The Image attributes are intensity and color.

Keywords

Semi Supervised Clustering, Image Segmentation, Em Clustering, Model Based Clustering.

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Rajammal P. Devadas ¹, T. M. Usha ¹, L. Sankari ¹, R. S. Rajalakshmi ¹, Geetha Patrath ¹, Mangala Babtiwale ¹

Affiliations
1 Sri Avinashilingam Home Science College, Coimbatore, IN

Abstract

Nutrition is one of the major factors responsible for the maintenance of health and physical fitness of man. Diet and nutrition surveys so for carried out in India have revealed that the diets consumed by a large majority of the population are based mainly on cereals and contain negligibile quantities of protective and protein rich foods.

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Godavari Kamalanathan ¹, L. Sankari ¹, Rajammal P. Devadas ¹

Affiliations
1 Sri Avinashilingam Home Science College, Coimbatore, IN

Abstract

Diets based predominantly on rice and containing only small amounts of milk and other protective foods are being consumed by a vast majority of the population in different parts of India.' Aykroyd and Krishnan^ showed in experiments with albino rats that poor Indian rice diets are deficient in calcium, vitamin A, certain vitamins of the B complex group and proteins.

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P. SanthanaLakshmi ¹, L. Sankari ²

Affiliations
1 Department of Information Technology, KG College of Arts and Science, Bharathiar University, Coimbatore, IN
2 Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women, Bharathiar University, IN

Abstract

In video surveillance the video broadcasting takes a major role for inferring the information. Noise existences in the video obscure important details, due to this the quality of images are negotiated in video frames. In this regard it is important to reduce the noise from video frames. One of the video pre processing techniques is noise removal in video frames. There are several existing approaches available to different types of noises in video frames. In this proposed method we adapted the fuzzy based filtering, in this the fuzzy based noise determination and fuzzy based denoising technique is applied by generating fuzzy rule. Since there is a massive deal of eliminate noise from video content, this paper has been committed to noise detection and filtering technology with the aim of eliminate unwanted noise without distressing it unconstructively the clarity of scenes that contain essential detail and hasty motion. In this proposed work the experimental result was conducted using existing denoising filters.

Keywords

Video Processing, Frames, Denoising Filters, Noise and Spatial Video.

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