This paper presents experimental work on mammogram image analysis. Texture analysis is carried out using segmentation technique. Here, statistical method have been used to extract features from the segmented tumour area. The obtained features are classified using different classifiers such as Radial basis function, Main Feed forward and Main Fitnet method. The method was tested on 100 clinical ...

We present an algorithm for the on-board vision vehicle detection problem using a cascade of boosted classifiers. Three families of features are compared: the rectangular filters (Haar-like features), the histograms of oriented gradient (HoG), and their combination (a concatenation of the two preceding features). A comparative study of the results of the generative (HoG features), discriminativ...

AbstrAct Relevance.In.Co-SVM algorithm, color and texture are naturally considered as sufficient and.uncorrelated.views.of.an.image..SVM classifier is learned in color and texture feature subspaces, respectively. Then the two classifiers are used to classify the unlabeled data. These unlabeled samples that disagree in the two classifiers are chose to label. The extensive experiments show that t...

Classification of various rice types and determination of its quality is a major issue in the scientific and commercial fields associated with modern agriculture. In recent years, various image processing techniques are used to identify different types of agricultural products. There are also various color and texture-based features in order to achieve the desired results in this area. In this ...

In this paper, we describe our participation in the ImageCLEF 2015 Scalable Concept Image Annotation task. In this participation, we propose an approach of image annotation by using ontology at several steps of supervised learning with noisy unlabeled data. In this regard, we construct tree-like ontology for each annotating concept of images using WordNet and Wikipedia. The constructed ontologi...