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Classification of Images Using Active Learning

Publication Type : Journal Article

Publisher : International Journal of Applied Engineering Research

Source : International Journal of Applied Engineering Research, Research India Publications, Volume 10, Number 8, p.21185-21197 (2015)

Url :

Campus : Coimbatore

School : School of Engineering

Department : Computer Science

Year : 2015

Abstract : Nowadays image classification has become an evolving area. Various methods are used for different classification. In image classification, there are many different machine learning algorithms used. In the database the retrieve images which are stored that is used to find the resemblance in the query image, then the CBIR allows the user to represent a query image. From image database it will retrieve all the images which belongs to a particular category and occurs problem in the search category. To achieve higher image accuracy within less execution time, classification of images is an intricate process which is essential to classify, organize and access them using an efficient, faster and easy way. The main motivation is to examine the performance of algorithm and check whether that algorithm is better suited for classification. By finding the accuracy of the classification, time and cost complexity can be low. The features can be extracted for both training and testing process in different set of images. The visual features of image such as texture, shape, color, etc. is a technique used by CBIR in which the user will search the image from large image database and represent the image in the form of a query according to the request of the user. The main objective is to classify images using active learning. By analyzing the active learning contribution in CBIR, different classification strategies are explained and compared. © Research India Publications.

Cite this Research Publication : E. Shehnaz and Dr. Venkataraman D., “Classification of Images Using Active Learning”, International Journal of Applied Engineering Research, vol. 10, pp. 21185-21197, 2015.

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