• IEIE Transactions on Smart Processing and Computing
• /
• 제5권3호
• /
• pp.153-163
• /
• 2016

Local feature extraction methods for images and videos are widely applied in the fields of image understanding and computer vision. However, robust features are detected differently when using the latest feature detectors and descriptors because of diverse image environments. This paper analyzes various feature extraction methods by summarizing algorithms, specifying properties, and comparing performance. We analyze eight feature extraction methods. The performance of feature extraction in various image environments is compared and evaluated. As a result, the feature detectors and descriptors can be used adaptively for image sequences captured under various image environments. Also, the evaluation of feature detectors and descriptors can be applied to driving assistance systems, closed circuit televisions (CCTVs), robot vision, etc.

• Journal of information and communication convergence engineering
• /
• 제21권1호
• /
• pp.82-89
• /
• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• 정보처리학회논문지:소프트웨어 및 데이터공학
• /
• 제2권4호
• /
• pp.273-280
• /
• 2013

본 논문에서는 의료영상의 특성을 반영하여 픽셀 그래디언트의 방향 값을 특징으로 하는 OCS-LBP (Oriented Center Symmetric Local Binary Patterns) 특징을 개발하고 BoF(Bag-of-Feature)와 Random Forest 분류기를 이용한 영상 검색 방법을 제안한다. 학습영상에서 추출된 특징 값은 code book 으로 군집화 되고, 각 영상들은 code book을 통해 의미 있는 새로운 차원인 BoF특징으로 변환된다. 이렇게 추출된 BoF특징은 Random Forest 분류기에 적용되고 학습된 분류기에 의해 유사한 특성을 갖는 N개의 클래스별로 분류되게 된다. 질의 영상이 입력되면 동일한 OCS-LBP특징이 추출되고 code book을 통해 BoF특징이 추출된다. 전통적인 내용기반 영상검색과는 다르게, 본 논문에서는 질의 영상에서 추출된 BoF특징이 학습된 Random Forest에 적용되어 가장 유사한 K-근접 이웃 (K-nearest neighbor) 클래스들을 선택하고 선택된 클래스들에 포함된 영상들에 대해서만 질의 영상과의 BoF 유사도 측정을 통해 최종 유사한 영상을 검색하게 된다. 실험결과에서 본 논문에서 제안하는 방법은 빠르고 우수한 검색 성능을 보여 주었다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2002년도 ITC-CSCC -3
• /
• pp.1420-1423
• /
• 2002

The classification and recognition of two-dimensional trademark patterns independently of their position, orientation, size and scale by proposing two feature vectors has been discussed. The paper presents experimentation on two feature vectors showing size- invariance and scale-invariance respectively. Both feature vectors are equally invariant to rotation as well. The feature extraction is based on local as well as global statistics of the image. These feature vectors have appealing mathematical simplicity and are versatile. The results so far have shown the best performance of the developed system based on these unique sets of feature. The goal has been achieved by segmenting the image using connected-component (nearest neighbours) algorithm. Second part of this work considers the possibility of using back propagation neural networks (BPN) for the learning and matching tasks, by simply feeding the feature vectosr. The effectiveness of the proposed feature vectors is tested with various trademarks, not used in learning phase.

• 전자공학회논문지
• /
• 제51권11호
• /
• pp.181-186
• /
• 2014

본 논문에서는 보행자 상반신 검출기의 성능을 향상하기 위한 세그먼테이션에 기반한 특징 추출 방법을 제안한다. 상반신의 부분별 색상 분포를 활용한 멀티 파트 컬러 세그먼테이션을 사용하여 국소 특징이 갖는 한계로 인해 발생하는 오검출의 감소에 효과적인 "전역적" 윤곽 특징을 추출한다. 컬러 공간과 히스토그램 분해도에 따른 성능을 분석하였으며, 자체 구축한 보행자 상반신 영상을 사용한 실험을 통해서 제안한 방법으로 추출한 특징이 국소 특징 기반 검출기의 오검출 감소에 효과적임을 확인하였다.

• 반도체디스플레이기술학회지
• /
• 제14권4호
• /
• pp.56-60
• /
• 2015

Mobile devices like smartphones and tablets are becoming increasingly capable in terms of processing power. Although they are already used in computer vision, no comparable measurement experiments of the popular feature extraction algorithm have been made yet. That is, local feature descriptors are widely used in many computer vision applications, and recently various methods have been proposed. While there are many evaluations have focused on various aspects of local features, matching accuracy, however there are no comparisons considering on speed trade-offs of recent descriptors such as ORB, FAST and BRISK. In this paper, we try to provide a performance evaluation of feature descriptors, and compare their matching precision and speed in KD-Tree setup with efficient computation of Hamming distance. The experimental results show that the recently proposed real valued descriptors such as ORB and FAST outperform state-of-the-art descriptors such SIFT and SURF in both, speed-up efficiency and precision/recall.

• Journal of Electrical Engineering and Technology
• /
• 제12권1호
• /
• pp.363-372
• /
• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제9권10호
• /
• pp.4126-4142
• /
• 2015

Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.

• 전기전자학회논문지
• /
• 제19권4호
• /
• pp.526-534
• /
• 2015

In this paper, we studied the use of the foreground and background features and SVM classifier to improve the accuracy of offline handwritten numeral recognition. The foreground features are two directional features: directional gradient feature by Kirsch operators and directional stroke feature by local shrinking and expanding operations, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where the concavity feature functions as complement to the directional features. During classification of the numeral, these three features are combined to obtain good discrimination power. The efficiency of our scheme is tested by recognition experiments on the handwritten numeral database CENPARMI, where SVM classifier with RBF kernel is used. The experimental results show the usefulness of our scheme and recognition rate of 99.10% is achieved.

• 한국컴퓨터정보학회논문지
• /
• 제11권3호
• /
• pp.11-20
• /
• 2006

본 논문에서는 칼라 영상의 스케치 특징점을 추출하기 위해 퍼지신경회로망을 이용하는 방법에 대하여 설명한다. 이 신경회로망은 스케치 특징점 추출을 위한 퍼지 소속함수를 학습시킴으로써 적절한 국부 임계 치를 획득할 수 있도록 구성된다. 제안한 퍼지신경회로망의 입출력 소속함수는 표준영상으로부터 추출된 최적의 특징점 추출결과를 기반으로 구성하여 학습 데이타로 사용된다. 학습에 사용된 퍼지입력변수는 디지털 영상에서의 특징점 추출 시 국부영역 밝기를 잘 반영할 뿐만 아니라 특징점 추출성능이 매우 우수한 특성이 있으며, 이들 입력변수의 소속함수를 신경회로망으로 학습시킴으로써 매우 효과적이고 신속하게 스케치 특징점들을 추출할 수 있다. 실험결과, 소속함수로 학습된 신경회로망으로부터 얻어진 임계치를 사용한 특징점 추출이 다양한 영상에 대하여 매우 우수함을 보였다.