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Scene Classification Based on Color and Texture Features
, M.Sc. Thesis Sharif University of Technology ; Jamzad, Mansour (Supervisor)
Abstract
Scene classification is one of the most controversial fields in computer vision. It has many applications such as robot navigation and control, content-based image retrieval (CBIR), semantic organization of image databases, depth estimation and multimedia services. In fact the outcome of any classification system depends on the ability of the feature vector defined for the problem, by means of its distinguishing strength. In this research we focus on efficient feature extraction methods. In recent years, methods based on bags of features and special pyramid approach, have shown good performance in scene classification comparison to the others. So we based our proposed method on these ideas....
A feature fusion based localized multiple kernel learning system for real world image classification
, Article Eurasip Journal on Image and Video Processing ; Volume 2017, Issue 1 , 2017 ; 16875176 (ISSN) ; Jamzad, M ; Sharif University of Technology
Abstract
Real-world image classification, which aims to determine the semantic class of un-labeled images, is a challenging task. In this paper, we focus on two challenges of image classification and propose a method to address both of them simultaneously. The first challenge is that representing images by heterogeneous features, such as color, shape and texture, helps to provide better classification accuracy. The second challenge comes from dissimilarities in the visual appearance of images from the same class (intra class variance) and similarities between images from different classes (inter class relationship). In addition to these two challenges, we should note that the feature space of...