期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2017
卷号：5
期号：1
页码：268
DOI：10.15680/IJIRCCE.2017.0501040
出版社：S&S Publications
摘要：A new classification approach is proposed for pollen grains. Along with image statistics and shapedescriptor, histogram (H) coefficient features were used as input to the classifier. As the earlier reported approacheswerefound to be tedious and time consuming with less accuracy, the present approach gives precise accuracy inclassification of pollen grains by using SEM images. The improved classifiers based on Generalized Feed Forward(GFF) Neural Network, Modular Neural Network (MNN), Principal Component Analysis (PCA) Neural Network, andSupport Vector Machine (SVM) are explored with optimization of their respective parameters in view of reduction intime as well as space complexity. In order to reduce the space complexity, sensitivity analysis is done to eliminatetheinsignificant parameters from the dataset.As performance of all these neural networks is compared with respect toMSE, NMSE and the Average Classification Accuracy (ACA), GFF NN comprising of two hidden layers is found to besuperior (95 % ACA on CV dataset) to all other classifiers. The new improved classifier algorithm with Histogramcoefficients provides more accuracy as compared to the earlier algorithms, which usedDiscrete Cosine TransformFeatures and Walsh Hadamard Transform coefficients. The robustness of the classifier to noise is verified on the Crossvalidation dataset by introducing controlled Gaussian and Uniform noise in both input and output. The proposedapproach is inexpensive, reliable and nearly accurate that can be used without help of experts from the field ofpalynology.
关键词：Pollen SEM images;palynology; Computational Intelligence; Multi-layer Perceptron Neural Network;Principal Component Analysis; Support Vector Machine; Classifier.
