• 한국융합학회논문지
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• 제9권4호
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• pp.47-55
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• 2018

본 논문에서는 얼굴인식 성능 향상을 위해 얼굴 지역 영역 영상들로 학습된 다중개의 심층 합성곱 신경망(Deep Convolutional Neural Network)으로부터 추출된 심층 지역 특징들(Deep local features)을 가중치를 부여하여 결합하는 방법을 제안한다. 제안 방법에서는 지역 영역 집합으로 학습된 다중개의 심층 합성곱 신경망으로부터 추출된 심층 지역 특징들과 해당 지역 영역의 중요도를 나타내는 가중치들을 결합한 특징표현인 '가중치 결합 심층 지역 특징'을 형성한다. 일반화 얼굴인식 성능을 극대화하기 위해, 검증 데이터 집합(validation set)을 사용하여 지역 영역에 해당하는 가중치들을 계산하고 가중치 집합(weight set)을 형성한다. 가중치 결합 심층 지역 특징은 조인트 베이시안(Joint Bayesian) 유사도 학습방법과 최근접 이웃 분류기(Nearest Neighbor classifier)에 적용되어 테스트 얼굴영상의 신원(identity)을 분류하는데 활용된다. 제안 방법은 얼굴영상의 자세, 표정, 조명 변화에 강인하고 기존 최신 방법들과 비교하여 얼굴인식 성능을 향상시킬 수 있음이 체계적인 실험을 통해 검증되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.3972-3988
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• 2020

Existing methods always rely on statistical features to extract local words for microblog user geolocation. There are many non-local words in extracted words, which makes geolocation accuracy lower. Considering the statistical and semantic features of local words, this paper proposes a microblog user geolocation method by extracting local words based on word clustering and wrapper feature selection. First, ordinary words without positional indications are initially filtered based on statistical features. Second, a word clustering algorithm based on word vectors is proposed. The remaining semantically similar words are clustered together based on the distance of word vectors with semantic meanings. Next, a wrapper feature selection algorithm based on sequential backward subset search is proposed. The cluster subset with the best geolocation effect is selected. Words in selected cluster subset are extracted as local words. Finally, the Naive Bayes classifier is trained based on local words to geolocate the microblog user. The proposed method is validated based on two different types of microblog data - Twitter and Weibo. The results show that the proposed method outperforms existing two typical methods based on statistical features in terms of accuracy, precision, recall, and F1-score.

• 대한전자공학회논문지
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• 제25권5호
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• pp.559-565
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• 1988

A new valley operator is presented for extracting sketch features which contain valleys and edges subject to local intensities. It is a very simple operator using the local probablities in a 3x3 local window. Experimental results show its excellent performance over the existing valley or edge operators.

• 한국군사과학기술학회지
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• 제15권3호
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• pp.298-305
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• 2012

In this paper, we propose an integrated algorithm for scene recognition, which has been a challenging computer vision problem, with application to mobile robot localization. The proposed scene recognition method utilizes SIFT and visual words as local-level features and GIST as a global-level feature. As local-level and global-level features complement each other, it results in improved performance for scene recognition. This improved algorithm is of low computational complexity and robust to image distortions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권4호
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• pp.819-833
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• 2013

Computing global features based on local features using a bio-inspired framework has shown promising performance. However, for some tough applications with large intra-class variances, a single local feature is inadequate to represent all the attributes of the images. To integrate the complementary abilities of multiple local features, in this paper we have extended the efficacy of the bio-inspired framework, HMAX, to adapt heterogeneous features for global feature extraction. Given multiple global features, we propose an approach, designated as parameterized metric learning, for high dimensional feature fusion. The fusion parameters are solved by maximizing the canonical correlation with respect to the parameters. Experimental results show that our method achieves significant improvements over the benchmark bio-inspired framework, HMAX, and other related methods on the Caltech dataset, under varying numbers of training samples and feature elements.

• Journal of Information Processing Systems
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• 제13권6호
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• pp.1628-1639
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• 2017

The combination texture feature extraction approach for texture image retrieval is proposed in this paper. Two kinds of low level texture features were combined in the approach. One of them was extracted from singular value decomposition (SVD) based dual-tree complex wavelet transform (DTCWT) coefficients, and the other one was extracted from multi-scale local binary patterns (LBPs). The fusion features of SVD based multi-directional wavelet features and multi-scale LBP features have short dimensions of feature vector. The comparing experiments are conducted on Brodatz and Vistex datasets. According to the experimental results, the proposed method has a relatively better performance in aspect of retrieval accuracy and time complexity upon the existing methods.

• 전자공학회논문지B
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• 제30B권4호
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• pp.1-10
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• 1993

In this paper, a new method of phoneme segmentation of handwritten Korean characters using the local graph pattern is proposed. At first, thinning was performed before extracting features. End-point, inflexion-point, branch-point and cross-point were extracted as features. Using these features and the angular relations between these features, local graph pattern was made. When local graph pattern is made, the of strokes is investigated on contacting point. From this process, pattern is simplified as contacting pattern of the basic form and the contacting form we must take into account can be restricted within fixed region, 4therefore phoneme segmentation not influenced by characters form and any other contact in a single character is performed as matching this local graph pattern with base patterns searched ahead. This experiments with 540 characters have been conducted. From the result of this experiment, it is shown that phoneme segmentation is independent of characters form and other contact in a single character to obtain a correct segmentation rate of 95%, manages it efficiently to reduce the time spent in lock operation when the lock.

• 한국통신학회논문지
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• 제39A권2호
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• pp.92-99
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• 2014

본 논문은 결정 트리(Decision tree) 구조를 기반으로 한 표정 인식 방법을 제안한다. ASM(Active Shape Model)과 LBP(Local Binary Pattern)를 통해, 표정 영상들의 국소 특징들을 추출한다. 국소 특징들로부터 표정들을 잘 분류할 수 있는 판별 특징(Discriminant feature)들을 추출하고, 그 판별 특징들은 모든 조합의 각 두 가지 표정들을 분류시킨다. 분류를 통해 얻어진 정인식의 합을 통해, 정인식 최대화 기반 국소 영역과 표정 조합을 결정한다. 이 가지 분류들을 종합하여, 결정 트리를 생성한다. 이 결정 트리 기반 표정 인식률은 약 84.7%로, 결정 트리를 고려하지 않은 방법보다, 더 좋은 인식 성능을 보였다.

• 로봇학회논문지
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• 제8권3호
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• pp.206-216
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• 2013

We investigate the role of contact location information on the perception of local features during contour following in a virtual environment. An absolute identification experiment is conducted under force-alone and force-plus-contact-location conditions to investigate the effect of the contact location information. The results show that the participants identify the local features significantly better in terms of higher information transfer for the force-plus-contact-location condition, while no significant difference was found for measures of the efficacy of contour following between the two conditions. Further data analyses indicate that the improved identification of local features with contact location information is due to the improved identification of small surface features.

• Journal of Multimedia Information System
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• 제1권2호
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• pp.119-126
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• 2014

We propose a bicycle detection system riding on people based on modified projected local binary pattern(PLBP) for vision based intelligent vehicles. Projection method has robustness for rotation invariant and reducing dimensionality for original image. The features of Local binary pattern(LBP) are fast to compute and simple to implement for object recognition and texture classification area. Moreover, We use uniform pattern to remove the noise. This paper suggests that modified LBP method and projection vector having different weighting values according to the local shape and area in the image. Also our system maintains the simplicity of evaluation of traditional formulation while being more discriminative. Our experimental results show that a bicycle and motorcycle riding on people detection system based on proposed PLBP features achieve higher detection accuracy rate than traditional features.