Curvelet Based Multiclass Image Classification Under Complex Background Using Neural Network
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Object detection and classification is very important part in images having complex background in computer vision. The objective of this work is to construct a classification system for identification of multiclass object images surrounded by complex back ground. The object images of three classes are taken from various databases for training and testing the performance of classifier. A blocking model is presented for feature extraction of the image after pre processing of the image. The curvelet features are extracted from each of the block of the image and then applied to neural network based classifier. The performance of the system is evaluated and compared by three experiments with varying block numbers. Experimental results exhibits that method is effective and give the better results than other method of feature extraction using statistical and wavelet
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