EKSTRAKSI FITUR BERBASIS WAVELET PADA SISTEM TEMU KEMBALI CITRA TEKSTUR

Pada penelitian ini diimplementasikan suatu sistem temu kembali citra tekstur dengan ekstraksi fitur berbasis wavelet. Ekstraksi fitur terdiri dari 2 proses utama, yaitu dekomposisi wavelet terhadap data citra, dan perhitungan energi dan deviasi standar terhadap koefisien-koefisien wavelet hasil dekomposisi. Penelitian ini bertujuan untuk mengetahui performance sistem temu kembali citra tekstur dengan menggunakan beberapa vektor fitur yang diekstrak dari beberapa level dekomposisi wavelet yang berbeda. Untuk uji coba, dibuat database citra menggunakan citra tekstur Brodatz. Filter yang digunakan pada proses dekomposisi diturunkan dari fungsi wavelet Daubechies4, dan untuk perhitungan kemiripan antara citra contoh dan citra dalam database digunakan Canberra distance. Hasil ujicoba menunjukkan bahwa performance terbaik dari sistem temu kembali citra tekstur adalah recall sebesar 92,5%, yang didapat dengan menggunakan vektor fitur yang diekstrak dari hasil dekomposisi wavelet sampai dengan level 2. Kata kunci: Temu kembali citra tekstur,Ttransformasi wavelet, Canberra distanc

Premise Parameter Optimization on Adaptive Network Based Fuzzy Inference System Using Modification Hybrid Particle Swarm Optimization and Genetic Algorithm

ANFIS is a combination of the Fuzzy Inference System (FIS) and Neural Network (NN), which has two training parameters, premise and consequent. In the traditional ANFIS, Least Square Estimator (LSE) and Gradient Descent (GD) are commonly used learning algorithms to train the two parameters. The combination of those two learning algorithms tends to produce the local optimal solution. Particle Swarm Optimization (PSO) can converge quickly but still allow for getting the local optimal solution because PSO is unable to find a new solution space. Meanwhile, Genetic Algorithm (GA) has been reported to be able to find a wider solution space. Hybrid PSOGA is expected to give a better solution. In this study, modification of hybrid PSOGA is used to train the premise parameter of ANFIS. In experiments, the accuracy of the proposed classification method, which is called ANFIS-PSOGA, is compared to ANFIS-GA and ANFIS-PSO on Iris flowers, Haberman, and Vertebral datasets. The experiment shows that ANFIS-PSOGA achieves the best result compared to the other methods, with an average of accuracy 99.85% on Iris flowers, 84.52% on Haberman, and 91.83% on Vertebral

Combination of fast hybrid classification and k value optimization in k-nn for video face recognition

Nowadays, the need for face recognition is no longer include images only but also videos. However, there are some challenges associated with the addition of this new technique such as how to determine the right pre-processing, feature extraction, and classification methods to obtain excellent performance. Although nowadays the k-Nearest Neighbor (k-NN) is widely used, high computational costs due to numerous features of the dataset and large amount of training data makes adequate processing difficult. Several studies have been conducted to improve the performance of k-NN using the FHC (Fast Hybrid Classification) method by optimizing the local k values. One of the disadvantages of the FHC Method is that the k value used is still in the default form. Therefore, this research proposes the use of k-NN value optimization methods in FHC, thereby, increasing its accuracy. The Fast Hybrid Classification which combines the k-means clustering with k-NN, groups the training data into several prototypes called TLDS (Two Level Data Structure). Furthermore, two classification levels are applied to label test data, with the first used to determine the n number of prototypes with the same class in the test data. The second classification using the optimized k value in the k-NN method, is employed to sharpen the accuracy, when the same number of prototypes does not reach n. The evaluation results show that this method provides 86% accuracy and time performance of 3.3 seconds

CHARACTER IMAGE SEGMENTATION OF JAVANESE SCRIPT USING CONNECTED COMPONENT METHOD

Automation of Javanese script translation is needed to make it easier for people to understand the meaning of ancient Javanese script. By using Javanese script image as input, the translation system generally consists of character segmentation, character recognition, and combining the recognized characters as a meaningful word. The segmentation which obtains region of interest of each character, is an important process in the translation system. In the previous research, segmentation using projection profile method can separate each character well. The method can overcome characters overlapping, but it still produces truncated characters. In this study, we proposed a new segmentation to reduce the truncated character. The first step of the proposed method is pre-processing that consists of converting input into binary image and cleaning noises. The next step is to determine the connected component labels, which further perform as candidate of characters. Some of the candidates are still represented by more than one labels, so that we need a process to merge the connected component labels that have centroid distance less than threshold. We evaluate the proposed method using Intersection over Union (IoU). The evaluation shows the best accuracy 93,26%

Selective local binary pattern with convolutional neural network for facial expression recognition

Variation in images in terms of head pose and illumination is a challenge in facial expression recognition. This research presents a hybrid approach that combines the conventional and deep learning, to improve facial expression recognition performance and aims to solve the challenge. We propose a selective local binary pattern (SLBP) method to obtain a more stable image representation fed to the learning process in convolutional neural network (CNN). In the preprocessing stage, we use adaptive gamma transformation to reduce illumination variability. The proposed SLBP selects the discriminant features in facial images with head pose variation using the median-based standard deviation of local binary pattern images. We experimented on the Karolinska directed emotional faces (KDEF) dataset containing thousands of images with variations in head pose and illumination and Japanese female facial expression (JAFFE) dataset containing seven facial expressions of Japanese females’ frontal faces. The experiments show that the proposed method is superior compared to the other related approaches with an accuracy of 92.21% on KDEF dataset and 94.28% on JAFFE dataset

Median Filter For Transition Region Refinement In Image Segmentation

Transition region based image segmentation is one of the simple and effective image segmentation methods. This method is capable to segment image contains single or multiple objects. However, this method depends on the background. It may produce a bad segmentation result if the gray level variance is high or the background is textured. So a method to repair the transition region is needed. In this study, a new method to repair the transition region with median filter based on the percentage of the adjacent transitional pixels is proposed. Transition region is extracted from the grayscale image. Transition region refinement is conducted based on the percentage of the adjacent transitional pixels. Then, several morphological operations and the edge linking process are conducted to the transition region. Afterward, region filling is used to get the foreground area. Finally, image of segmentation result is obtained by showing the pixels of grayscale image that are located in the foreground area. The value of misclassification error (ME), false negative rate (FNR), and false positive rate (FPR) of the segmentation result are calculated to measure the proposed method performance. Performance of the proposed method is compared with the other method. The experimental results show that the proposed method has average value of ME, FPR, and FNR: 0.0297, 0.0209, and 0.0828 respectively. It defines that the proposed method has better performance than the other methods. Furthermore, the proposed method works well on the image with a variety of background, especially on image with textured background

MODIFIED LOCAL TERNARY PATTERN WITH CONVOLUTIONAL NEURAL NETWORK FOR FACE EXPRESSION RECOGNITION

Facial expression recognition (FER) on images with illumination variation and noises is a challenging problem in the computer vision field. We solve this using deep learning approaches that have been successfully applied in various fields, especially in uncontrolled input conditions. We apply a sequence of processes including face detection, normalization, augmentation, and texture representation, to develop FER based on Convolutional Neural Network (CNN). The combination of TanTriggs normalization technique and Adaptive Gaussian Transformation Method is used to reduce light variation. The number of images is augmented using a geometric augmentation technique to prevent overfitting due to lack of training data. We propose a representation of Modified Local Ternary Pattern (Modified LTP) texture image that is more discriminating and less sensitive to noise by combining the upper and lower parts of the original LTP using the logical AND operation followed by average calculation. The Modified LTP texture images are then used to train a CNN-based classification model. Experiments on the KDEF dataset show that the proposed approach provides a promising result with an accuracy of 81.15%

IMPROVING ROBUSTNESS OF FACE EXPRESSION RECOGNITION USING MULTI-CHANNEL LOCAL BINARY PATTERN AND NEURAL NETWORK

ABSTRACTFacial Expression Recognition (FER) is a subset of Artificial Intelligence (AI) that relates to human non-verbal communication. The development of Convolutional Neural Network (CNN) based FER is subject to noise, mainly because of the usage of RGB Original Image as training data. Many research explored texture feature methods which noise resistant, such as Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM), which mainly worked on grayscale images. Multi-Channel Local Binary Pattern (MCLBP) is derived from LBP which analyzes texture on color images.This research aims to develop FER using MCLBP as a method of hand-crafted texture feature and NN as a classification method. The combination of MCLBP and Neural Network (NN) is expected more robust to noise. First, preprocessing is applied to the facial image for contrasting with Adaptive Gamma Correction Weighted Distribution (AGCWD). Next, the facial image is converted to MCLBP images. Then MCLBP images are converted to vectors as a NN architecture training data with 5 Fully Connected layers. Batch Normalization and Rectified Linear Unit (ReLu) activation are used in every Fully Connected layer. At the last Fully Connected Layer, ReLu activation was replaced with SoftMax activation. This NN uses Stochastic Gradient Descend (SGD) optimizer with a learning rate of 0.005.Performance testing was held by comparing the epoch required to reach F1-score 1 and F1-Score from many scenarios in FER with LBP + NN with 140 × 190 image size, LBP + NN with 70 × 85 image size, and MCLBP + NN with 70 × 85 image size approaches. From all scenarios we have tried, the best method is MCLBP with F1-Score =1 in 22 epochs. The method of hand-crafted texture feature with NN can increase the desirable FER performances.                                                                                       Keywords: Local Binary Pattern, Multi-Channel LBP, Neural Network, Face Expression Recognition, Gamma Correctio

FRACTAL DIMENSION AND LACUNARITY COMBINATION FOR PLANT LEAF CLASSIFICATION

Plants play important roles for the existence of all beings in the world. High diversity of plant’s species make a manual observation of plants classifying becomes very difficult. Fractal dimension is widely known feature descriptor for shape or texture. It is utilized to determine the complexity of an object in a form of fractional dimension. On the other hand, lacunarity is a feature descriptor that able to determine the heterogeneity of a texture image. Lacunarity was not really exploited in many fields. Moreover, there are no significant research on fractal dimension and lacunarity combination in the study of automatic plant’s leaf classification. In this paper, we focused on combination of fractal dimension and lacunarity features extraction to yield better classification result. A box counting method is implemented to get the fractal dimension feature of leaf boundary and vein. Meanwhile, a gliding box algorithm is implemented to get the lacunarity feature of leaf texture. Using 626 leaves from flavia, experiment was conducted by analyzing the performance of both feature vectors, while considering the optimal box size r. Using support vector machine classifier, result shows that combined features able to reach 93.92 % of classification accuracy