Informatics Publishing Limited

D. N. Vasundhara ¹, M. Seetha ²

Affiliations
1 Jawaharlal Nehru Technological University, Hyderabad, Telangana, IN
2 Department of Computer Science Engineering, G Narayanamma Institute of Technology and Science, Hyderabad, Telangana, IN

Abstract

Image classification is one of classical problem for many aspects of remote sensing in which to extract some of the important spatially variable parameters, global change studies and environmental applications. In the literature, various classification methods have been developed for classification of images such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Fuzzy Support Vector Machines (FSVM), Genetic algorithms (GA) and Decision Trees (DT). In this paper, we propose a combined scheme for spatial image classification, which is composed of FSVM and ANN techniques. The proposed classification scheme consists of two main steps: Firstly, we separate the each image into class by an ANN classifier based on the features of images and in the second step, the FSVM classifier has been applied on the output of ANN.  This can be denoted as ANN_FSVM classifier. A comparison of techniques for spatial data has been given in this paper.

Keywords

Artificial Neural Network, Feature Extraction, Fuzzy Support Vector Machine, Image Classification.

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K. Prasanna ¹, M. Seetha ², M. Sankara Prasanna Kumar ³

Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology & Sciences, Rajampet, Andhra Pradesh, IN
2 Department of Computer Science and Engineering, G. Narayanamma Institute of Technology & Sciences for Woman, Hyderabad, Andhra Pradesh, IN
3 Department of Computer Science and Engineering, Annamacharya Institute of Technology & Sciences, Rajampet, IN

Abstract

Association rule mining aims at generating association rules between sets of items in a database. Now a day, due to huge accumulation in the database technology and incredible growth in high dimensional dataset, conventional data base methods are inadequate in extracting useful information. Such large high dimensional data gives rise to a number of new computational challenges not only the increased in number of data objects but also in the increased in number of features/attributes. However, it is becoming very tedious to generate association rules from high dimensional data, because it contains different dimensions or attributes in the large data bases. To improve the high dimensional data mining task, it must be preprocessed efficiently and accurately. In this paper, an Apriori based method for generating association rules from large high dimensional data is proposed. It constitutes 1) Preprocessing and generalizing the data base dimensions; 2) generating high dimensional strong association rules using support and confidence. It can be seen from experiments that the mining algorithm is elegant and efficient, which can obtain more rapid computing speed and sententious rules at the same time It was ascertained that the proposed method is proved to be better in support of generating association rules.

Keywords

Association Analysis, Apriori Algorithm, Pre Processing, High Dimensional Data, Support, Confidence, Data Mining.

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G. P. Hegde ¹, M. Seetha ², Nagaratna Hegde ³

Affiliations
1 Department of Computer Science and Engineering, Sri Dharmasthala Manjunatheshwara Institute of Technology, Ujire, Visvesvaraya Technological University, Belgaum, IN
2 Department of Computer Science and Engineering, GNITS, Hyderabad, IN
3 Department of Computer Science and Engineering, VCE, Hyderabad, IN

Abstract

Human face expression is one of the cognitive activity or attribute to deliver the opinions to others. This paper mainly delivers the performance of appearance based holistic approach subspace methods based on Principal Component Analysis (PCA). In this work texture features are extracted from face images using Gabor filter. It was observed that extracted texture feature vector space has higher dimensional and has more number of redundant contents. Hence training, testing and classification time becomes more. The expression recognition accuracy rate is also reduced. To overcome this problem Symmetrical Weighted 2DPCA (SW2DPCA) subspace method is introduced. Extracted feature vector space is projected in to subspace by using SW2DPCA method. By implementing weighted principles on odd and even symmetrical decomposition space of training samples sets proposed method have been formed. Conventional PCA and 2DPCA method yields less recognition rate due to larger variations in expressions and light due to more number of feature space redundant variants. Proposed SW2DPCA method optimizes this problem by reducing redundant contents and discarding unequal variants. In this work a well known JAFFE databases is used for experiments and tested with proposed SW2DPCA algorithm. From the experimental results it was found that facial recognition accuracy rate of GF+SW2DPCA based feature fusion subspace method has been increased to 95.24% compared to 2DPCA method.

Keywords

Subspace, Gabor Filter, Expression Recognition, Symmetrical Weight, Feature Extraction, Classifier.

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