Unsupervised Hybrid Classification for Texture Analysis Using Fixed and Optimal Window Size
Journal Title: International Journal on Computer Science and Engineering - Year 2010, Vol 2, Issue 9
Abstract
For achieving better classification results in texture analysis, it is to combine different classification methods. Though there are existing methods which have been using fixed window size that resulted lack of classification accuracy and in order to improve the classification accuracy, the window size must be increased. Moreover the optimal window size is to be selected is also an important thing in the improvement of better classification output. In addition, since some classification techniques are used for micro textured structures and some are for large scale textured images, it is better to integrate different classification methods to achieve higher classification rate. This paper presents a new classification technique named unsupervised hybrid classification for texture analysis (UHCTA) that extracts the properties of different methods for achieving higher classification rate. Also comparison with the existing methods conform the merits of the proposed unsupervised ybrid classification for texture analysis method in terms of ccuracy in various image conditions.
Authors and Affiliations
S. S SREEJA MOLE , Dr. L. GANESAN
Keywords
Related Articles
- EP ID EP97292
- DOI -
- Views 114
- Downloads 0
TEMPORAL SEQUENTIAL PATTERN IN DATA MINING TASKS
The rapid increase in the data available leads to the difficulty for analyzing those data and different types of frameworks are required for unearthing useful knowledge that can be extracted from such databases. The fiel...
Relational Peer Data Sharing Settings and Consistent Query Answers
In this paper, we study the problem of consistent query answering in peer data sharing systems. In a peer data sharing system, databases in peers are designed and administered autonomously and acquaintances between peers...
Feature Extraction Technique for Neural Network Based Pattern Recognition
In this work, an attempt is made to extract minimum number of features to represent the pattern used as inputs for Feed Forward Back Propagation Neural Network (FFBPNN). The binary image of a pattern stored in the frame...
Hamiltonian cycle and TSP: A backtracking approach
Backtracking is one of the strategies to reduce the complexity of a problem. Backtracking mainly useful when there is a no solution by going forward in that direction so we required backtracking from it to reduce the com...
AN EFFICIENT TEXT CLASSIFICATION USING KNN AND NAIVE BAYESIAN
The main objective is to propose a text classification based on the features selection and preprocessing thereby reducing the dimensionality of the Feature vector and increase the classification accuracy. Text classifica...