ABSTRACT:With the number of channels in the hundreds instead of in the tens Hyper spectral imagery possesses much richer spectral information than multispectral imagery. The increased dimensionality of such Hyper spectral data provides a challenge to the current technique for analyzing data. Conventional classification methods may not be useful without dimension reduction pre-processing. So dimension reduction has become a significant part of Hyper spectral image processing. This paper presents a comparative analysis of the efficacy of Haar and Daubechies wavelets for dimensionality reduction in achieving image classification. Spectral data reduction using Wavelet Decomposition could be useful because it preserves the distinction among spectral signatures. Daubechies wavelets optimally capture the polynomial trends while Haar wavelet is discontinuous and resembles a step function. The performance of these wavelets are compared in terms of classification accuracy and time complexity. This paper shows that wavelet reduction has more separate classes and yields better or comparable classification accuracy. In the context of the dimensionality reduction algorithm, it is found that the performance of classification of Daubechies wavelets is better as compared to Haar wavelet while Daubechies takes more time compare to Haar wavelet. The experimental results demonstrate the classification system consistently provides over 84% classification accuracy.
Object classifier often operates by making decisions based on the values of several shape properties measured from an image of the object. The paper introduces a unique definition of measure for 2-D geometrical object shape detection. Using this definition different object shapes can be identified on the basis of their degree of fitness parameter. Basically, we have fitted a 2-D polygon/curve on the object as a best fitted polygon/curve and computed the parameter degree of fitness which is the ratio of the matching area and non-matching area due to the fitted polygon/curve and the object both. The results show the effectiveness of the proposed measure.
A support vector machine (SVM) is not a popular method for a very large dataset classification because the training and testing time for such data are computationally expensive. Many researchers try to reduce the training time of SVMs by applying sample reduction methods. Many methods reduced the training samples by using a clustering technique. To reduce its high computational complexity, several data reduction methods were proposed in previous studies. However, such methods are not effective to extract informative patterns. This paper demonstrates a new supervised classification method, multiseed-based SVM (MSB-SVM), which is particularly intended to deal with very large datasets for multiclass classification. The main contributions of the paper are (i) an efficient multiseed technique for selection of seed points from circular/elongated class training samples, (ii) adjacent class pair selection from the set of multiseeds by using the minimum spanning tree, and (iii) extraction of support vectors from class pair seed equivalent regions to manage multiclass classification problems without being computationally expensive. Experimental results on a variety of datasets showed better performance compared to other sample-reducing methods in terms of training and testing time. Traditional support vector machine (SVM) solution suffers from O(n 2 ) time complexity, which makes it impractical for very large datasets. Here, multiseed point technique depends on the estimated density of each data, and the order of computation is O(n log n) . Using the estimated density, the computational cost of the seed selection algorithm is O(n) . So, this is the only burden for reducing the sample. However, reducing the sample takes less time with the proposed algorithm compared to the clustering methods. At the same time, the number of support vectors has been abruptly reduced, which takes less time to find the decision surface. Apart from this, the classification accuracy of the proposed technique is significantly better than other existing sample reduction methods especially for large datasets.
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