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This paper aims to do a comparative study of local feature descriptor techniques and convolutional neural networks (CNN) for retrieving Thai silk pattern images. Two feature descriptor techniques, the histogram of oriented gradients and the scale-invariant feature transform, are compared to extract feature vectors from the silk pattern images. We combined the feature vectors extracted from feature descriptor techniques with k-nearest neighbors (KNN) and support vector machine. Then we modified CNN architectures: LeNet-5 and AlexNet. The LeNet-5 was modified by increasing the number of neurons in each layer of the fully connected layers. The AlexNet architecture was modified by reducing the neurons in each layer of the fully connected layers. Finally, we evaluated the local descriptor techniques, the existing CNN architectures and our modified CNN architectures on Thai silk pattern dataset. The results of the study showed that the local descriptor techniques combined with KNN algorithm significantly outperform the CNN methods. [ABSTRACT FROM AUTHOR]

The use of/(-Nearest Neighbors (/cNN) algorithm for recognizing handwritten character scripts is presented in this survey paper. The kNN algorithm, the simple and well-known algorithm for machine learning, is suitable for classification scheme. According to the/cNN algorithm, unknown data is firstly compared with the training samples to compute the similarity function. The most similar k neighbors, which have the smallest distance values, are subsequently selected by using the majority vote. The unknown data is then assigned by determining the number of its occurrences in each class. The most frequent class in k neighbors is finally selected as the output of the classifier. เท this survey, it is found that the handwritten character datasets are collected from the different types of handwriting, including different character sets with the similar structure, a variety of the handwriting styles, incomplete handwritten characters and insufficient number of handwritten character samples. These factors have a directl impact on the performance of the handwritten character recognition. Thus, the robust feature extraction techniques for handwritten recognition can be combined with the/cNN algorithm in order to obtain the effective recognition performance. [ABSTRACT FROM AUTHOR]

In this paper, we propose a local descriptors approach to classify pornographic images. Two local descriptors including the scale-invariant feature transform (SIFT) and the histogram of oriented gradients (HOG) are computed feature vectors from pornographic images. The extracted features are supplied to the K-Nearest Neighbor Algorithm and Support Vector Machine for pornographic image classification. We have evaluated all different methods on the TI-UNRAM dataset. The results show that the HOG and the SIFT combined with the SVM significantly outperform the other methods, including KNN algorithm and image processing technique. Thus, the local descriptors approach can be combined with the SVM for pornographic image classification in order to obtain the effective classification performance. [ABSTRACT FROM AUTHOR]