• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5197-5218
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• 2019

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.194-197
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• 2000

Wavelet transform used for content-based image retrieval has good performance in texture image. Image features for content-based image retrieval are color, texture, and shape. In this paper, we use color feature extracted from HSI color space known as most similar vision system to human vision system and texture feature extracted from wavelet histogram which has multiresolution property. Proposed method is compared with HSI color histogram method and wavelet histogram method. It is shown better performance.

• 한국컴퓨터정보학회논문지
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• 제11권6호
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• pp.79-86
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• 2006

영상 식별 기술은 대용량의 멀티미디어 데이터베이스 환경 하에서 고속의 검색을 위해서 필수적이다. 본 논문은 이러한 고속 검색을 위하여 GA(Genetic Algorithm)과 SVM(Support Vector Machine)을 결합한 모델을 제안한다. 특징벡터로는 색상 정보와 질감 정보를 사용하였다. 이렇게 추출된 특징벡터의 집합을 제안한 모델을 통해 최적의 유효 특징벡터의 집합를 찾아 영상을 식별하여 정확도를 높였다. 성능평가는 색상, 질감. 색상과 질감의 연합 특징벡터를 각각 사용한 성능 비교. SYM과 제안된 알고리즘과의 성능을 비교하였다. 실험 결과 색상과 질감을 연합한 특징벡터를 사용한 것이 단일 특징벡터를 사용한 것 보다 좋은 결과를 보였으며 하이브리드 기법을 이용한 제안된 알고리즘이 SVM알고리즘만을 이용한 것 보다 좋은 결과를 보였다.

• 산업경영시스템학회지
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• 제27권3호
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• pp.59-65
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• 2004

This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

• Journal of Information Processing Systems
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• 제13권3호
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• pp.464-475
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• 2017

In this paper, we present a new texture image retrieval method which combines color and texture features extracted from images by a set of multi-resolution multi-direction (MRMD) filters. The MRMD filter set chosen is simple and can be separable to low and high frequency information, and provides efficient multi-resolution and multi-direction analysis. The color space used is HSV color space separable to hue, saturation, and value components, which are easily analyzed as showing characteristics similar to the human visual system. This experiment is conducted by comparing precision vs. recall of retrieval and feature vector dimensions. Images for experiments include Corel DB and VisTex DB; Corel_MR DB and VisTex_MR DB, which are transformed from the aforementioned two DBs to have multi-resolution images; and Corel_MD DB and VisTex_MD DB, transformed from the two DBs to have multi-direction images. According to the experimental results, the proposed method improves upon the existing methods in aspects of precision and recall of retrieval, and also reduces feature vector dimensions.

• Journal of Biosystems Engineering
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• 제37권4호
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• pp.252-257
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• 2012

Purpose: The net rind pattern and color of melon surface are important for a high market value of melon fruits. The development of the net and color are closely related to the changes in shape, size, and maturing. Therefore, the net and color characteristics can be used indicators for assessment of melon quality. The goal of this study was to investigate the possibility of estimating melon soluble solids content (SSC) and firmness by analyzing the net and color characteristics of fruit surface. Methods: The true color images of melon surface obtained at fruit equator were analyzed with 18 color features and 9 texture features. The partial least squares (PLS) method was used to estimate SSC and firmness in melons using their color and texture features. Results: In sensing melon SSC, the coefficients of determination of validation (${R_v}^2$) of the prediction models using the color and texture features were 0.84 (root mean square error of validation, RMSEV: 1.92 $^{\circ}Brix$) and 0.96 (RMSEV: 0.60 $^{\circ}Brix$), respectively. The ${R_v}^2$ values of the models for predicting melon firmness using the color and texture features were 0.64 (RMSEV: 4.62 N) and 0.79 (RMSEV: 2.99 N), respectively. Conclusions: In general, the texture features were more useful for estimating melon internal quality than the color features. However, to strengthen the usefulness of the color and texture features of melon surface for estimation of melon quality, additional experiments with more fruit samples need to be conducted.

• ETRI Journal
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• 제20권3호
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• pp.272-283
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• 1998

In this paper, we propose efficient content-based image retrieval methods using the automatic extraction of the low-level visual features as image content. Two new feature extraction methods are presented. The first one os an advanced color feature extraction derived from the modification of Stricker's method. The second one is a texture feature extraction using some DCT coefficients which represent some dominant directions and gray level variations of the image. In the experiment with an image database of 200 natural images, the proposed methods show higher performance than other methods. They can be combined into an efficient hierarchical retrieval method.

• 감성과학
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• 제7권4호
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• pp.57-65
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• 2004

본 논문에서는 color, texture, shape의 정보를 통합 이용하여 내용기반 영상검색 시스템의 성능을 향상시키는 기법을 고찰하였다. 먼저 영상에 내재되어 있는 color를 분석 추출하여 몇 개의 대표색으로 요약 표현한 다음, 이를 활용한 근사치 측정도를 고안하였다. Texture정보 분석에 있어서는 영상의 주축 행렬 데이터를 통계적 접근 방법으로 추출하였다. Edge분석의 방법으로는 Edge 막대그래프에서 색상변환, 양자화, 필터링에 관련된 정보를 선행처리 후 Edge 정보를 추출하였다. 마지막으로, 본 연구의 결과인 내용기반 영상검색 시스템의 효율성을 precision-recall 분석과 실험적 결과를 통하여 입증하였다.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.876-887
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• 2018

In this paper, we propose an robust CAMShift method to track stage objects with a camera. In order to solve the problem of tracking object misdetection in existing CAMShift technique, MBR region is detected to separate the background and the subject, and the subject size of the region of interest is calculated to solve the problem of erroneously detecting a large region having a similar color distribution ratio. Also, by applying the color corelogram and MB-LBP to the part that can not be solved by the color ratio and the size limitation, accurate texture tracking is enabled by reflecting the texture characteristics. Experimental results show that the proposed method has good tracking performance for objects that do not deviate from the size of the subject set in the area of interest and accurately extracts the texture characteristics of different subjects with similar color distribution ratios.

• 대한전자공학회논문지SP
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• 제42권5호
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• pp.103-114
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• 2005

본 논문에서는 칼라 특징으로 칼라 오토코렐로그램(autocorrelogram)을 선택하고 질감 특징으로 BDIP(block difference inverse probabilities)와 BVLC(block variance of local correlation coefficient)를 선택하여 이들을 효율적으로 추출하고 결합한 다중 특징기반 영상검색 기법을 제안한다. 칼라 오토코렐로그램은 영상의 H(hue), S(saturation) 칼라 성분으로부터 추출 하였고, BDIP와 BVLC는 V(value) 성분으로부터 추출하였다. 이때 각 특징추출 시 계산량을 고려하여 간소화된 오토코렐로그램과 BVLC를 제안하여 사용하였으며, 추출한 특징들을 효율적으로 저장하기 위해 특징벡터성분들의 값을 그 분포에 따라 균등 또는 비균등 양자화 하여 사용하였다. Corel DB및 VisTex DB에 대한 실험 결과, 칼라 오토코렐로그램과 BDIP, BVLC 질감 특징을 결합함으로써 동일한 차원에서 오토코렐로그램만을 사용할 때보다 최대 9.5%, BDIP, BVLC만을 사용할 때보다 최대 4% 검색성능이 향상되었다. 또한 제안한 다중 특징은 웨이브렛 모멘트, CSD, 칼라 히스토그램에 비해 특징벡터의 저장공간을 약 3분의 1 정도 적게 차지하면서 검색성능이 각각 최대 12.6%, 14.6%, 27.9% 우수하게 나타남을 확인할 수 있었다.