IJARAI Volume 3 Issue 2

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

View Full Issue

Paper 1: Static Gesture Recognition Combining Graph and Appearance Features

Abstract: In this paper we propose the combination of graph-based characteristics and appearance-based descriptors such as detected edges for modeling static gestures. Initially we convolve the original image with a Gaussian kernel and blur the image. Canny edges are then extracted. The blurring is performed in order to enhance some characteristics in the image that are crucial for the topology of the gesture (especially when the fingers are overlapping). There are a large number of properties that can describe a graph, one of which is the adjacency matrix that describes the topology of the graph itself. We approximate the topology of the hand utilizing Neural Gas with Competitive Hebbian Learning, generating a graph. From the graph we extract the Laplacian matrix and calculate its spectrum. Both canny edges and Laplacian spectrum are used as features. As a classifier we employ Linear Discriminant Analysis with Bayes’ Rule. We apply our method on a published American Sign Language dataset (ten classes) and the results are very promising and further study of this approach is imminent from the authors.

Author 1: Marimpis Avraam

Keywords: Gesture Recognition; Neural Gas; Linear Discriminant Analysis; Bayes Rule; Laplacian Matrix

PDF

Paper 2: Comparative Study of Feature Extraction Components from Several Wavelet Transformations for Ornamental Plants

Abstract: Human has a duty to preserve the nature, preserving the plant is one of the examples. This research emphasis on ornamental plant that has functionality not only as ornament plant but also as a medicinal plant. Purpose of this research is to find the best of the particular feature extraction components from several wavelet transformations. It consists of Daubechies, Dyadic, and Dual-tree complex wavelet transformation. Dyadic and Dual-tree complex wavelet transformations have shift invariant property. While Daubechies is a standard wavelet transform that widely used for many applications. This comparison is utilizing leaf image datasets from ornamental plants. From the experiments, obtained that best classification performance attained by Dual-tree complex wavelet transformation with 96.66% of overall performance result.

Author 1: Kohei Arai
Author 2: Indra Nugraha Abdullah
Author 3: Hiroshi Okumura

Keywords: wavelet transformation; shift invariant; rotation invariant; feature extraction; leaf identification

PDF

Paper 3: Human Gait Gender Classification using 3D Discrete Wavelet Transform Feature Extraction

Abstract: Feature extraction for gait recognition has been created widely. The ancestor for this task is divided into two parts, model based and free-model based. Model-based approaches obtain a set of static or dynamic skeleton parameters via modeling or tracking body components such as limbs, legs, arms and thighs. Model-free approaches focus on shapes of silhouettes or the entire movement of physical bodies. Model-free approaches are insensitive to the quality of silhouettes. Its advantage is a low computational costs comparing to model-based approaches. However, they are usually not robust to viewpoints and scale. Imaging technology also developed quickly this decades. Motion capture (mocap) device integrated with motion sensor has an expensive price and can only be owned by big animation studio. Fortunately now already existed Kinect camera equipped with depth sensor image in the market with very low price compare to any mocap device. Of course the accuracy not as good as the expensive one, but using some preprocessing method we can remove the jittery and noisy in the 3D skeleton points. Our proposed method is to analyze the effectiveness of 3D skeleton feature extraction using 3D Discrete Wavelet Transforms (3D DWT). We use Kinect Camera to get the depth data. We use Ipisoft mocap software to extract 3d skeleton model from Kinect video. From the experimental results shows 83.75% correctly classified instances using SVM.

Author 1: Kohei Arai
Author 2: Rosa Andrie Asmara

Keywords: gender gait classification; 3D Skeleton Model; SVM; Biometrics; 3D DWT

PDF

Paper 4: Method for Traffic Flow Estimation using On-dashboard Camera Image

Abstract: This paper presents the method to estimate the traffic flow on the urban roadway by using car’s on-dashboard camera image. The system described, shows something new which utilizes only road traffic photo images to get the information about urban roadway traffic flow automatically.

Author 1: Kohei Arai
Author 2: Steven Ray Sentinuwo

Keywords: traffic flow estimation; on-dashboard camera; computer vision.

PDF

Paper 5: Predicting Quality of Answer in Collaborative Question Answer Learning

Abstract: Studies over the years shown that students had actively and more interactively involved in a classroom discussion to gain their knowledge. By posting questions for other participants to answer, students could obtain several answers to their question. The problem is sometimes the answer chosen by student as the best answer is not necessarily the best quality answer. Therefore, an automatic recommender system based on student activity, may improve these situations as it will choose the best answer objectively. On the other side, in the implementation of collaborative learning, in addition to sharing information, sometimes students also need a reference or domain knowledge which relevant with the topic. In this paper, we proposed answer quality predictor in collaborative question answer (CQA) learning, to predict the quality of answer either from recommender system based on users activity or domain knowledge as reference information.

Author 1: Kohei Arai
Author 2: ANIK Nur Handayani

Keywords: collaborative question answer learning; domain knowledge; answer quality predictor; recommender

PDF

Paper 6: Preliminary Study on Phytoplankton Distribution Changes Monitoring for the Intensive Study Area of the Ariake Sea, Japan Based on Remote Sensing Satellite Data

Abstract: Phytoplankton distribution changes in the Ariake Sea areas, Japan based on remote sensing satellite data is studied. Through experiments with Terra and AQUA MODIS data derived chlorophyll-a concentration and suspended solid as well as truth data of chlorophyll-a concentration together with meteorological data and tidal data which are acquired 7 months from October 2012 to April 2013, it is found that strong correlation between the truth data of chlorophyll-a and MODIS derived chlorophyll-a concentrations with R square value ranges from 0.677 to 0.791. Also it is found that the relations between ocean wind speed and chlorophyll-a concentration as well as between tidal effects and chlorophyll-a concentration. Meanwhile, there is a relatively high correlation between sunshine duration a day and chlorophyll-a concentration.

Author 1: Kohei Arai
Author 2: Toshiya Katano

Keywords: chlorophyl-a concentration; suspended solid; ocean winds

PDF

Paper 7: Using Unlabeled Data to Improve Inductive Models by Incorporating Transductive Models

Abstract: This paper shows how to use labeled and unlabeled data to improve inductive models with the help of transductivemodels.We proposed a solution for the self-training scenario. Self- training is an effective semi-supervised wrapper method which can generalize any type of supervised inductive model to the semi-supervised settings. it iteratively refines a inductive model by bootstrap from unlabeled data. Standard self-training uses the classifier model(trained on labeled examples) to label and select candidates from the unlabeled training set, which may be problematic since the initial classifier may not be able to provide highly confident predictions as labeled training data is always rare. As a result, it could always suffer from introducing too much wrongly labeled candidates to the labeled training set, which may severely degrades performance. To tackle this problem, we propose a novel self-training style algorithm which incorporate a graph-based transductive model in the self-labeling process. Unlike standard self-training, our algorithm utilizes labeled and unlabeled data as a whole to label and select unlabeled examples for training set augmentation. A robust transductive model based on graph markov random walk is proposed, which exploits manifold assumption to output reliable predictions on unlabeled data using noisy labeled examples. The proposed algorithm can greatly minimize the risk of performance degradation due to accumulated noise in the training set. Experiments show that the proposed algorithm can effectively utilize unlabeled data to improve classification performance.

Author 1: ShengJun Cheng
Author 2: Jiafeng Liu
Author 3: XiangLong Tang

Keywords: Inductive model, Transductive model, Semi- supervised learning, Markov random walk

PDF

Paper 8: Migration Dynamics in Artificial Agent Societies

Abstract: An Artificial Agent Society can be defined as a collection of agents interacting with each other for some purpose and/or inhabiting a specific locality, possibly in accordance to some common norms/rules. These societies are analogous to human and ecological societies, and are an expanding and emerging field in research about social systems. Social networks, electronic markets and disaster management organizations can be viewed as such artificial (open) agent societies and can be best understood as computational societies. Members of such artificial agent societies are heterogeneous intelligent software agents which are operating locally and cooperating and coordinating with each other in order to achieve goals of an agent society. These artificial agent societies have some kind of dynamics existing in them in terms of dynamics of Agent Migration, Role-Assignment, Norm- Emergence, Security and Agent-Interaction. In this paper, we have described the dynamics of agent migration process, starting from the various types of agent migration, causes or reasons for agent migration, consequences of agent migration, and an agent migration framework to model the its behavior for migration of agents between societies.

Author 1: Harjot Kaur
Author 2: Karanjeet Singh Kahlon
Author 3: Rajinder Singh Virk

PDF