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Object Categorization Using Classemes and Metaclass by Linear SVM

Publication Date : 10/07/2015

Author(s) :

Mrs.Rubala N , Prof. V. S. Bhatlavande , Prof. M. S. Biradar .

Conference Name :
4th International Conference on Recent Trends in Engineering & Technology(ICRTET-2015) July 2-4,2015 Organized by SNJB's KBJ College of Engineering,Chandwad,Nashik,Maharashtra,India

Abstract :

This paper describes new descriptor for images which allows the construction of efficient and compact classifiers with good accuracy on object category recognition. The descriptors are the classifier that produces the features of each image. The descriptor is the output of a large number of weakly trained object category classifiers on the image. Unlike other attributes and hand defined classes, our features are learned automatically. We first train the classifier using dataset. Then by learning basis classes which gives us features information we are getting good accuracy. All methods are thoroughly evaluated on object classification datasets using a multitude of feature descriptors. The advantage of this descriptor is that it allows object-category queries to be made against image databases using efficient classifiers (efficient at test time). This paper also describes meta-classes which partitions the classes into subsets. In training phase we use support vector machine (SVM) classifier. The advantage of classemes using classifier is that training and testing can be done efficiently. We provide insight of when combination methods can be expected to work and how the benefit of complementary features can be exploited most efficiently.

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