Investigations on a few image Classification methods and Techniques for improving Classification performance

Abstract

As the number of images to be stored in personal collections public newlinedata sets and the internet is becoming huge and ever growing it becomes newlinecrucial to develop computationally efficient methods for organizing and newlinesearching images Therefore the ability to classify images into semantic newlinecategories and objects is essential in order to manage and organize the newlinecollection of images on a database The problem is challenging because the newlineappearance of object instances varies substantially owing to changes in pose newlineimaging and lighting conditions, occlusions and within class shape variations newlineIdeally the representation should be flexible enough to cover a wide range of newlinevisually different classes, each with large within category variations while newlineretaining good discriminative power between the classes In this research newlinethe problem of improving the classification performance of various classes newlineof images like objects semantic scenes fine grained textile designs and newline fabrics are explored from a supervised learning perspective Hence this thesis newlineis a result of the study and comparative evaluation of different methodologies for automatic image classification in terms of its degree of recognition using performance measures like accuracy rate Macro Precision Micro Precision Mean Average Precision MAP and F1 measure newline newline newline