in this paper, an unsupervised classification method using spatial contextual information for polarimetric sar (polsar) image classification is proposed. first, an unsupervised classification based on 2d h/▁α plane was performed, using cloude/pottier target decomposition algorithm. in order to compute the initial values of the cluster centers and hence a rapid convergence of the algorithm, the ...

Sparse coding is an unsupervised method which learns a set of over-complete bases to represent data such as image and video. Sparse coding has increasing attraction for image classification applications in recent years. But in the cases where we have some similar images from different classes, such as face recognition applications, different images may be classified into the same class, and hen...

The study presents an efficient gender classification technique. The gender of a facial image is the most prominent feature, and improvement in the existing gender classification methods will result in the high performance of the face retrieval and classification methods for large repositories. In this paper a new efficient gender classification method is proposed. First, the face part of the i...

A brain tumor arises due to an abnormal growth of cells that have proliferated in an uncontrolled manner. When normal cells grow old or get injured, they either undergo cell death or get repaired by own. Research shows that people affected by brain tumors die due to their inaccurate detection. In this paper,proposed an intelligent classification technique to recognize normal and abnormal MRI br...

We propose a novel local image descriptor called the Extended Multi-resolution Local Patterns, and a discriminative probabilistic framework for learning its parameters together with a multi-class image classifier. Our approach uses training data with image-level labels to learn the features which are discriminative for multi-class colonoscopy image classification. Experiments on a three class (...

We introduce an automated pathology classification system for medical volumetric brain image slices. Existing work often relies on handcrafted features extracted from automatic image segmentation. This is not only a challenging and time-consuming process, but it may also limit the adaptability and robustness of the system. We propose a novel approach to combine sparse Gabor-feature based classi...

An approach for automatic classification of computed tomography (CT) medical images is presented in this paper. A vast amount of CT images are produced in modern hospitals due to advances of multi-slice Computed Tomography (CT) Scan which handles up to 64 slices per scan. So, an input image based automatic medical image retrieval system is now a necessity. In this paper, Coiflet wavelets are us...

Medical images are crucial in diagnosis, therapy, surgery, reference, and training. The availability of Digital radiology equipment availability has ensured that digital medical images management gets more attention now. This paper presents an automatic classification system for Computed Tomography (CT) medical images are presented in this paper. In the presented methodology, Bi-orthogonal spli...