عنوان البحث(Papers / Research Title)
Object Oriented Classification of Forest Images
الناشر \ المحرر \ الكاتب (Author / Editor / Publisher)
توفيق عبد الخالق عباس الاسدي
Citation Information
توفيق,عبد,الخالق,عباس,الاسدي ,Object Oriented Classification of Forest Images , Time 5/22/2011 6:24:08 AM : كلية تكنولوجيا المعلومات
وصف الابستركت (Abstract)
Object Oriented Classification of Forest Images
الوصف الكامل (Full Abstract)
Object Oriented Classification of Forest Images UsingSoft Computing Approach
Tawfiq A. Abbas
1*, Samaher Hussein Ali 2** and Israa Hadi Ali 3** Department of Computer Science, University of Babylon, Iraqtawfiqasadi63@yahoo.com** Department of Computer Science, University of Babylon, IraqSamaher_hussein@yahoo.com
Abstract:
In this paper the searching capability of build up an object oriented classification system which is capable of classification a given forest scene into its various constituents. To simplify the problem, six categories of forest structures were defined. These categories are trees, bushes, grasses, foliage, sky and background sky .They are sufficient to represent typical forest scenes dealt with in this application. To implement such classification system. We proposed a Genetic Algorithm (GA) to segmentation image and find the best seed for each category. According to this scheme, an image is divided evenly into small block. Then it is processed block by block. For each block, Discrete Cosine Transform (DCT) is applied to determine some of DCT coefficient in compressed domain as the feature vectors.Then take the seed values for each segment and the DCT coefficients to represented the inputs of feed forward neural network. These system successes in classification all objects in image although used different kinds of activation functions (hyperbolic functions), compares among them and find the best of it in obtaining on fast results. As a result the A soft computing method will be higher classification accuracy than that of traditional pixel-based supervised classification and gives convenient environment to use.Key words: Discrete Cosine Transform, genetic algorithm, neural network, object oriented classification, Soft Computing.
Introduction:
he image classification process consists of three major phases. The first phase is called image segmentation, or object isolation, in which each object is found, and its image is isolated from the rest of the scene. Because of its intuitive properties and simplicity of implementation [1]. The second phase is called feature extraction. This is where the objects are measured. A measurement is the value of some quantifiable property of an object. A feature is a function of one or more measurement, computed so that it quantifies some significant characteristic of the object [2]. The feature extraction process produces a set of features that, taken together, comprise the feature vector. Feature extraction drastically reduces the amount of information that represents all the knowledge upon which the subsequent classification decisions must be based. It is productive to conceptualize an n-dimensional space in which all possible n-element feature vectors reside. Thus, any particular object corresponds to a point in feature space.
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