• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.1964-1981
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• 2014

An improved Mean-Shift (MS) tracker called joint CB-LBWH, which uses a combined weighted-histogram scheme of CBWH (Corrected Background-Weighted Histogram) and LBWH (likelihood-based Background-Weighted Histogram), is presented. Joint CB-LBWH is based on the notion that target representation employs both feature saliency and confidence to form a compound weighted histogram criterion. As the more prominent and confident features mean more significant for tracking the target, the tuned histogram by joint CB-LBWH can reduce the interference of background in target localization effectively. Comparative experimental results show that the proposed joint CB-LBWH scheme can significantly improve the efficiency and robustness of MS tracker when heavy occlusions and complex scenes exist.

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.185-190
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• 2010

In this paper we propose the CBIR algorithm which is based on a novel image block method that combined both color and edge feature. The main drawback of global histogram representation is dependent of the color without spatial or shape information, a new image block method that divided the image to 8 related blocks which contained more information of the image is utilized to extract image feature. Based on these 8 blocks, histogram equalization and edge detection techniques are also used for image retrieval. The experimental results show that the proposed image block method has better ability of characterizing the image contents than traditional block method and can perform the retrieval system efficiently.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1671-1679
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• 2004

This study has proposed visual-feature extraction methods for each band in wavelet domain with both spatial frequency features and multi resolution features. In addition, it has brought forward similarity measurement method using fuzzy theory and new color feature expression method taking advantage of the frequency of the same color after color quantization for reducing quantization error, a disadvantage of the existing color histogram intersection method. Experiments are performed on a database containing 1,000 color images. The proposed method gives better performance than the conventional method in both objective and subjective performance evaluation.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1357-1367
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• 2013

Recently, as one of images based methods in facial expression recognition, the research which used ULBP block histogram feature and SVM classifier was performed. Due to the properties of LBP introduced by Ojala, such as highly distinction capability, durability to the illumination changes and simple operation, LBP is widely used in the field of image recognition. In this paper, we combined $LBP_{8,2}$ and $LBP_{8,1}$ to describe micro features in addition to shift, size change in calculating ULBP block histogram. From sub-windows of 660 of $LBP_{8,1}$ and 550 of $LBP_{8,2}$, ULBP histogram feature of 1210 were extracted and weak classifiers of 50 were generated using AdaBoost. By using the combined $LBP_{8,1}$ and $LBP_{8,2}$ hybrid type of ULBP histogram feature and SVM classifier, facial expression recognition rate could be improved and it was confirmed through various experiments. Facial expression recognition rate of 96.3% by hybrid boosted ULBP block histogram showed the superiority of the proposed method.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.13-21
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• 2019

The content based on image retrieval makes use of features of information within image such as color, texture and share for Retrieval data. we present a novel approach for improving retrieval accuracy based on DCT Filter-Bank. First, we perform DCT on a given image, and generate a Filter-Bank using the DCT coefficients for each color channel. In this step, DC and the limited number of AC coefficients are used. Next, a feature vector is obtained from the histogram of the quantized DC coefficients. Then, AC coefficients in the Filter-Bank are separated into three main groups indicating horizontal, vertical, and diagonal edge directions, respectively, according to their spatial-frequency properties. Each directional group creates its histogram after employing Otsu binarization technique. Finally, we project each histogram on the horizontal and vertical axes, and generate a feature vector for each group. The computed DC and AC feature vectors bins are concatenated, and it is used in the similarity checking procedure. We experimented using 1,000 databases, and as a result, this approach outperformed the old retrieval method which used color information.

• International Journal of Contents
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• v.8 no.1
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• pp.30-38
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• 2012

Text extraction and binarization are the important pre-processing steps for text recognition. The performance of text binarization strongly related to the accuracy of recognition stage. In our proposed method, the first stage based on line detection and shape feature analysis applied to locate the position of a business card and detect the shape from the complex environment. In the second stage, several local regions contained the possible text components are separated based on the projection histogram. In each local region, the pixels grouped into several connected components based on the connected component labeling and projection histogram. Then, classify each connect component into text region and reject the non-text region based on the feature information analysis such as size of connected component and stroke width estimation.

• Journal of Multimedia Information System
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• v.4 no.2
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• pp.79-82
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• 2017

Smoke and fire have different shapes and colours. This article suggests a fully connected system which is used two features using Adaboost algorithm for constructing a strong classifier as linear combination. We calculate the local histogram feature by gradient and bin, local binary pattern value, and projection vectors for each cell. According to the histogram magnitude, this paper applied adapted weighting value to improve the recognition rate. To preserve the local region and shape feature which has edge intensity, this paper processed the normalization sequence. For the extracted features, this paper Adaboost algorithm which makes strong classification to classify the objects. Our smoke detection system based on the proposed approach leads to higher detection accuracy than other system.

• International Journal of Contents
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• v.9 no.3
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• pp.1-9
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• 2013

In this paper, we propose a fast and accurate system for detecting pedestrians from a static image. Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian detection systems but extracting HOG is expensive due to its high dimensional vector. It will cause long processing time and large memory consumption in case of making a pedestrian detection system on high resolution image or video. In order to deal with this problem, we use Principal Components Analysis (PCA) technique to reduce the dimensionality of HOG. The output of PCA will be input for a linear SVM classifier for learning and testing. The experiment results showed that our proposed method reduces processing time but still maintains the similar detection rate. We got twenty five times faster than original HOG feature.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.812-820
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• 2012

In this paper, multiple pedestrians tracking system using Histogram of Oriented Gradient and occlusion detection is proposed. The proposed system is applicable to Intelligent Surveillance System. First, we detect pedestrian in a image sequence using pedestrian's feature. To get pedestrian's feature, we make block-histogram using gradient's direction histogram based on HOG(Histogram of Oriented Gradient), after that a pedestrian region is classified by using Linear-SVM(Support Vector Machine) training. Next, moving objects are tracked by using position information of the classified pedestrians. And we create motion trajectory descriptor which is used for content based event retrieval. The experimental results show that the proposed method is more fast, accurate and effective than conventional methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.268-273
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• 2009

Iris recognition is a biometric technology which can identify a person using the iris pattern. It is important for the iris recognition system to extract the feature which is invariant to changes in iris patterns. Those changes can be occurred by the influence of lights, changes in the size of the pupil, and head tilting. In this paper, we propose a novel method based on local gradient orientation histogram which is robust to variations in illumination and rotations of iris patterns. The proposed method enables high-speed feature extraction and feature comparison because it requires no additional processing to obtain the rotation invariance, and shows comparable performance to the well-known previous methods.