Informatics Publishing Limited

Neetesh Gupta ¹, Vijay Anant Athavale ², MD. Ilyas Khan ¹

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
2 SVS Group of Institutions, Ganga Nahar, Hastinapur Road, Mawana, Merrut, IN

Abstract

Digital imagery has expanded its horizon in many directions, resulting in an explosion in the volume of image data required to be organized. While most traditional image retrieval systems perform searches using comparisons of text based strings, content based systems extract features from the content of an image to judge its similarity with another. For the purpose of image retrieval is presented in this paper. The image retrieval problem is motivated by the need to search the exponentially increasing space of image and video databases efficiently and effectively. We Extract Low level feature like as color, Texture, shape etc. and calculate similarity or dissimilarity between archieve of images. Finally we implement a user friendly Graphical system with Relevance feedback of image retrieval and finally quality assessment of similarity is evaluated.

Keywords

CBIR, Color, Texture, Shape Feature Extraction, Image Assessment, GUI, Similarity Measurement.

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Neetesh Gupta ¹, Niket Bhargava ², Md. Ilyas Khan ¹, Shiv Kumar ³, Bhupendra Verma ⁴

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology, Bhopal (M.P.), IN
2 Department of Computer Science, Bansal Institute of Technology, Bhopal (M.P.), IN
3 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
4 Department of Computer Science, Technocrat Institute of Technology, Bhopal (M.P.), IN

Abstract

For the purpose of content-based image retrieval (CBIR) An up-to-date comparison of state-of-the-art low-level color and texture feature extraction approach is presented in this paper. The CBIR problem is identified by us because there is a need to search the huge databases having images efficiently and effectively. in this paper we suggest a color and texture feature extraction algorithms. Special attention is given for CBIR is the similarity measurement using correlation coefficient with distinct distance matrices properties. A New approach for image retrieval technique is proposed to improve retrieval performance, and reduce the extraction search times. Matching is performed between the test image and the object image and quality of matching is measured in terms of grading.

Keywords

Similarity, Measurement, CBIR, Low Level Feature Extraction, Correlation Coefficient.

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Shiv Kumar ¹, Vijay K. Chaudhari ², Md. Ilyas Khan ³, Neetesh Gupta ⁴, Bupendra Verma ⁵

Affiliations
1 Technocrat Institute of Technology-Bhopal (M.P.), IN
2 Department of Information Technology at Technocrat Institute of Technology-Bhopal (M.P.), IN
3 Technocrats Institute of Technology, Bhopal, IN
4 Department of CSE/IT at Technocrat Institute of Technology-Bhopal (M.P.), IN
5 Gandhi Technical University, Bhopal(M.P.), IN

Abstract

In this paper “Genes Analysis of Data by Using Hierarchical Quality Threshold Clustering” is an approach which proposed dynamically Growing Hierarchical Self Organizing Map (DGHSOM) with Nano array to identify co-expressed genes. The DGHSOM overcomes the problem of specifying the number of clusters and total number of iteration before the processing now, we are using QT (quality threshold) clustering is a method of partitioning data, which is invented for gene clustering. It requires more computing power than k- means, but does not require specifying the number of clusters. DNA Nano array technology is a challenging area in bioinformatics research, as we have to monitor millions of genes simultaneously. The expression profile of the gene can be useful in cancer disease analysis and its diagnosis. Gene expression data is very voluminous and very difficult to analyze. Several clustering algorithm have been proposed to identify co expressed genes. The Self-organizing-maps (SOM) is a powerful tool for recognizing and classifying features in complex, micro array data. But the interpretation of co- expression of genes are heavily depends on domain knowledge and SOM lacks since the number of clusters must be determined before training.

Keywords

Gene Expression Profile, Image Processing, Dynamically Growing Self Organizing Map, Nano Array, Qt Clustering.

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