Informatics Publishing Limited

I. Samuel Peter James ¹, D. Magdalene Delighta Angeline ²

Affiliations
1 Department of Computer Science and Engineering, Chandy College of Engineering, Thoothukudi, IN
2 Department of Computer Science and Engineering, Dr. G. U. Pope College of Engineering, Sawyerpuram, IN

Abstract

The main objective of this paper is to retrieve image based on visual features where CBIR technique is used. CBIR performs retrieval based on the similarity defined in terms of extracted features with more objectiveness. Due to the enormous increase in image db sizes, as well as its vast deployment in various applications, the need for CBIR development arose. In this paper, the features like shape, texture, edge, HSV colour are extracted, feature extracted values are used to find the similarity between input query image and the db image. The image is ranked according to the minimum distance value.

Keywords

Colour, Content Based Image Retrieval, Gray Scale, Phong Shading, Texture.

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D. Magdalene Delighta Angeline ¹, I. Samuel Peter James ²

Affiliations
1 Department of Computer Science and Engineering, Dr. G.U. Pope College of Engineering, Sawyerpuram, IN
2 Department of Computer Science and Engineering, Chandy College of Engineering, Thoothukudi, IN

Abstract

Texture analysis in computer vision aims at the problems of feature extraction, segmentation and classification, synthesis, and inferring shape from texture. The main objective of this project is to analyze the texture and segment it using textur models. The three stages in this project are texture analysis, edg detection and segmentation. In the first stage, to extract feature, w propose a Regularized Demodulation Algorithm which provides more robust texture features. Second stage is edge detection that facilitates the estimation of posterior probabilities for the edge and texture classes. Third is segmentation that is based on DCA features which uses curve evolution implemented with level set methods With DCA a low-dimensional, yet rich texture feature vector that proves to be useful for texture segmentation.

Keywords

AM-FM Models, Cue Combination, Curve Evolution, Demodulation, Generative Models, Image Segmentation, Texture Analysis.

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D. Magdalene Delighta Angeline ¹, I. Samuel Peter James ²

Affiliations
1 Department of Computer Science and Engineering, Dr. G.U. Pope College of Engineering, Sawyerpuram-628251, Tamilnadu, IN
2 Department of Computer Science and Engineering, Chandy College of Engineering, Thoothukudi, Tamilnadu, IN

Abstract

Association rules reflect the inner relationship of data. Discovering these associations is beneficial to the correct and appropriate decision made by decision-makers. It also provides an effective means to found the potential link between the data, reflecting a built-in association between the data. In order to show the effective relation of data, student placement was chosen and experiments were carried out which shows the best rules with 92.86% confidence while comparing with the previous Apriori approach. In this paper Apriori Mend algorithm was discussed which provide better result in mining association rules for Student's placement in industry.

Keywords

Apriori Mend Algorithm, Association Rules, Data Mining, Knowledge Discovery.

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