In a content based image classification system, target images are sorted by feature similarities with respect to the query (CBIR). In this paper, we propose to use new approach combining distance tangent, k-means algorithm and Bayesian network for image classification. First, we use the technique of tangent distance to calculate several tangent spaces representing the same image. The objective ...

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

We present a system for automated image annotation which is capable of detecting concepts such as sky, water and man-made structure in color images. The processing of each image consists of a feature extraction stage and a grouping stage. The ensuing concept-recognition is accomplished using a decision tree. The pixel-level features consist of basic color and texture information. The color info...

In this paper, we present a novel methodology to detect and recognize objects in cluttered scenes by proposing boosted contextual descriptors of landmarks in a framework of multi-class object recognition. To detect a sample of the object class, Boosted Landmarks identify landmark candidates in the image and define a constellation of contextual descriptors able to capture the spatial relationshi...

We demonstrate a stochastic gradient algorithm that can handle the very large number of stump features generated by considering every possible threshold over numerical, or continuous, features. Our problem is to classify data with continuous features, where small variations in the feature value can result in a different classification decision. Consider for instance packet statistics used for n...