• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1941-1945
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• 2011

In this paper, we propose a real-time classifier to distinguish humans from livestock by using the spatial integral. The image-difference method and the Expectation Maximization are used to reduce noises in input image. A histogram analysis based on Simulated Annealing and the fuzzy-Bayesian algorithm are used to classify human and livestock. Finally, the experiment results show the validity of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1877-1885
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• 2015

This paper presents a new Nearest-Neighbors based weighted representation for images and weighted K-Nearest-Neighbors (WKNN) classifier to improve the precision of image classification using the Bag of Visual Words (BOVW) based models. Scale-invariant feature transform (SIFT) features are firstly extracted from images. Then, the K-means++ algorithm is adopted in place of the conventional K-means algorithm to generate a more effective visual dictionary. Furthermore, the histogram of visual words becomes more expressive by utilizing the proposed weighted vector quantization (WVQ). Finally, WKNN classifier is applied to enhance the properties of the classification task between images in which similar levels of background noise are present. Average precision and absolute change degree are calculated to assess the classification performance and the stability of K-means++ algorithm, respectively. Experimental results on three diverse datasets: Caltech-101, Caltech-256 and PASCAL VOC 2011 show that the proposed WVQ method and WKNN method further improve the performance of classification.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.13-21
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• 1995

In this paper, we improve an image classifier algorithm based on neural network learning. It consists of two steps. The first is input pattern generation and the second, the global neural network implementation using an improved back-propagation algorithm. The feature vector for pattern recognition consists of the codebook data obtained from self-organization feature map learning. It decreases the input neuron number as well as the computational cost. The global neural network algorithm which is used in classifier inserts a control part and an address memory part to the back-propagation algorithm to control weights and unit-offsets. The simulation results show that it does not fall into the local minima and can implement easily the large-scale neural network. And it decreases largely the learning time.

• Journal of the Semiconductor & Display Technology
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• v.14 no.2
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• pp.29-34
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• 2015

Traffic lights are common in cities and are important cues for the path planning of intelligent vehicles. In this paper, we propose a robust and efficient algorithm for recognizing traffic lights from video sequences captured by a low cost off-the-shelf camera. Instead of using color information for recognizing traffic lights, a shape based approach is adopted. In learning and detection phase, Histogram of Oriented Gradients (HOG) feature is used and a cascade classifier based on Adaboost algorithm is adopted as the main classifier for locating traffic lights. To decide the color of the traffic light, a technique based on histogram analysis in HSV color space is utilized. Experimental results on several video sequences from typical urban environment prove the effectiveness of the proposed algorithm.

• Journal of KIISE:Software and Applications
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• v.29 no.7
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• pp.425-435
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• 2002

This paper introduces a macroblock-based rate control algorithm using the neural classifier based in Self Organization feature Maps (SOFM). In contrast to the conventional rate control methods based on the mathematical rate distortion (RD) model and the feedback regression, proposed method can actively adapt to the rapid-varying image characteristics by establishing the global model for bitrate control and by using the SOFM based neural classifier to manage that model. Proposed rate control algorithm has 0.2 dB ~ 0.6 dB better performances than MPEG-4 macroblock-based rate control algorithm by evaluating with the encoded Peak Signal to Noise Ratios while maintaining similar overall computational complexity.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.4
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• pp.289-293
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• 2002

Learning Classifier System (LCS) finds a new rule set using genetic algorithm (GA). In this paper, The Zeroth Level Classifier System (ZCS) is applied to evolving the strategy of a robot soccer simulation game (SimuroSot), which is a state varying dynamical system changed over time, as GBML (Genetic Based Machine Learning) and we show the effectiveness of the proposed scheme through the simulation of robot soccer.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2683-2702
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• 2015

A Content-based Image Retrieval (CBIR) system employs visual features rather than manual annotation of images. The selection of optimal features used in classification of images plays a key role in its performance. Category proliferation problem has a huge impact on performance of systems using Fuzzy Artmap (FAM) classifier. The proposed CBIR system uses a modified version of FAM called Non-Proliferation Fuzzy Artmap (NPFAM). This is developed by introducing significant changes in the learning process and the modified algorithm is evaluated by extensive experiments. Results have proved that NPFAM classifier generates a more compact rule set and performs better than FAM classifier. Accordingly, the CBIR system with NPFAM classifier yields good retrieval.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.2
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• pp.8-18
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• 1993

A multi-font, multi-size and high speed character recognition system is designed. The design principles are simpilcity of algorithm, adaptibility, learnability, hierachical data processing and attention by feed back. For the multi-size character recognition, the extracted character images are normalized. A hierachical classifier classifies the feature vectors. Feature is extracted by applying the directional receptive field after the directional dege filter processing. The hierachical classifier is consist of two pre-classifiers and one decision making classifier. The effect of two pre-classifiers is prediction to the final decision making classifier. With the pre-classifiers, the time to compute the distance of the final classifier is reduced. Recognition rate is 95% for the three documents printed in three kinds of fonts, total 1,700 characters. For high speed implemention, a multiprocessor system with the ring structure of four transputers is implemented, and the recognition speed of 30 characters per second is aquired.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.2
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• pp.102-106
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• 2003

This paper describes a new pattern classifier neural network to extract the feature from a letter. The proposed pattern classifier is based on relative distance, which is measure between an input datum and the center of cluster group. So, the proposed classifier neural network is called relative neural network(RNN). According to definitions of the distance and the learning rule, the structure of RNN is designed and the pseudo code of the algorithm is described. In feature extraction of letter, RNN, in spite of deletion of learning rate, resulted in the identical performance with those of winner-take-all(WTA), and self-organizing-map(SOM) neural network. Thus, it is shown that RNN is suitable to extract the feature of a letter.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.93-96
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• 2022

Natural language processing (NLP) is utilized to understand a natural text. Text analysis systems use natural language algorithms to find the meaning of large amounts of text. Text classification represents a basic task of NLP with a wide range of applications such as topic labeling, sentiment analysis, spam detection, and intent detection. The algorithm can transform user's unstructured thoughts into more structured data. In this work, a text classifier has been developed that uses academic admission and registration texts as input, analyzes its content, and then automatically assigns relevant tags such as admission, graduate school, and registration. In this work, the well-known algorithms support vector machine SVM and K-nearest neighbor (kNN) algorithms are used to develop the above-mentioned classifier. The obtained results showed that the SVM classifier outperformed the kNN classifier with an overall accuracy of 98.9%. in addition, the mean absolute error of SVM was 0.0064 while it was 0.0098 for kNN classifier. Based on the obtained results, the SVM is used to implement the academic text classification in this work.