Classification Techniques for Texture Images Using Neuroph Studio
With the widespread scope of image processing in fields such as ground classification, segmentation of satellite images, biomedical surface inspection and content-based image retrieval (CBIR), texture analysis is an important domain with a wide scope. The
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Abstract With the widespread scope of image processing in fields such as ground classification, segmentation of satellite images, biomedical surface inspection and content-based image retrieval (CBIR), texture analysis is an important domain with a wide scope. The process of texture analysis comprises the following important steps: texture classification, segmentation and synthesis. Texture is a significant property of images which is difficult to define even though the human eye may recognize it with ease. Texture classification has remained an intangible pattern recognition task despite numerous studies. The prime issue in any texture classification application is how to select the features and which features to consider for representing texture. Another major issue remains the type of metric to be used for comparing the feature vectors. The traditional way of texture classification is to convert the texture image into a vector representing the features using a set of filters. This is followed by classification with a few smoothening steps involved in between. This paper outlines a picture of the basic features of an image as texture, colour and shape which are to be extracted to form the feature vector. The concept of applying Machine learning for the purpose of classification is explored and implemented. The use of Soft Computing Technique—Artificial Neural Networks is illustrated for the purpose of classification. The texture features are extracted using the GLCM method and used as input and fed to the neural network. The algorithm used in training the neural networks is the traditional backpropagation algorithm. The results show which configuration of the multi-layer feedforward architecture is best suited according to our experimental set-up.
Priyanka Mahani (&) Akanksha Kulshreshtha Electronics and Computer Engineering Department, Dronacharya College of Engineering, Gurgaon, India e-mail: [email protected] Akanksha Kulshreshtha e-mail: [email protected] A.K. Goswami DTRL Lab, DRDO, Metcalfe House, New Delhi, India e-mail: [email protected] © Springer Science+Business Media Singapore 2016 M. Pant et al. (eds.), Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 437, DOI 10.1007/978-981-10-0451-3_33
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Priyanka Mahani et al.
Keywords Content-based image retrieval networks Backpropagation algorithm
Classification
Artificial neural
1 Introduction Classification means categorizing any physical entity or object into a predefined set of classes. Image classification is on the whole a difficult task for conventional machine learning algorithms due to the number of images involved and the variety of features that describe an image. As a result, traditional machines are not very stable for the purpose of classifying images from databases. Classification is a two-tier process which involves two tasks, namely learning and testing. The learning stage of classification involves building a prototype for each of the pr
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