• Title/Summary/Keyword: Global Spatial Matching

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Content-based Image Retrieval using the Color and Wavelet-based Texture Feature (색상특징과 웨이블렛 기반의 질감특징을 이용한 영상 검색)

  • 박종현;박순영;조완현;오일석
    • Journal of KIISE:Databases
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    • v.30 no.2
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    • pp.125-133
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    • 2003
  • In this paper we propose an efficient content-based image retrieval method using the color and wavelet based texture features. The color features are obtained from soft-color histograms of the global image and the wavelet-based texture features are obtained from the invariant moments of the high-pass sub-band through the spatial-frequency analysis of the wavelet transform. The proposed system, called a color and texture based two-step retrieval(CTBTR), is composed of two-step query operations for an efficient image retrieval. In the first-step matching operation, the color histogram features are used to filter out the dissimilar images quickly from a large image database. The second-step matching operation applies the wavelet based texture features to the retained set of images to retrieve all relevant images successfully. The experimental results show that the proposed algorithm yields more improved retrieval accuracy with computationally efficiency than the previous methods.

Fast and Accurate Visual Place Recognition Using Street-View Images

  • Lee, Keundong;Lee, Seungjae;Jung, Won Jo;Kim, Kee Tae
    • ETRI Journal
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    • v.39 no.1
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    • pp.97-107
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    • 2017
  • A fast and accurate building-level visual place recognition method built on an image-retrieval scheme using street-view images is proposed. Reference images generated from street-view images usually depict multiple buildings and confusing regions, such as roads, sky, and vehicles, which degrades retrieval accuracy and causes matching ambiguity. The proposed practical database refinement method uses informative reference image and keypoint selection. For database refinement, the method uses a spatial layout of the buildings in the reference image, specifically a building-identification mask image, which is obtained from a prebuilt three-dimensional model of the site. A global-positioning-system-aware retrieval structure is incorporated in it. To evaluate the method, we constructed a dataset over an area of $0.26km^2$. It was comprised of 38,700 reference images and corresponding building-identification mask images. The proposed method removed 25% of the database images using informative reference image selection. It achieved 85.6% recall of the top five candidates in 1.25 s of full processing. The method thus achieved high accuracy at a low computational complexity.

Large-eddy simulation of channel flow using a spectral domain-decomposition grid-embedding technique (스펙트럴 영역분할 격자 삽입법을 이용한 채널유동의 큰 에디 모사)

  • Gang, Sang-Mo;Byeon, Do-Yeong;Baek, Seung-Uk
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.22 no.7
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    • pp.1030-1040
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    • 1998
  • One of the main unresolved issues in large-eddy simulation(LES) of wall-bounded turbulent flows is the requirement of high spatial resolution in the near-wall region, especially in the spanwise direction. Such high resolution required in the near-wall region is generally used throughout the computational domain, making simulations of high Reynolds number, complex-geometry flows prohibitive. A grid-embedding strategy using a nonconforming spectral domain-decomposition method is proposed to address this limitation. This method provides an efficient way of clustering grid points in the near-wall region with spectral accuracy. LES of transitional and turbulent channel flow has been performed to evaluate the proposed grid-embedding technique. The computational domain is divided into three subdomains to resolve the near-wall regions in the spanwise direction. Spectral patching collocation methods are used for the grid-embedding and appropriate conditions are suggested for the interface matching. Results of LES using the grid-embedding strategy are promising compared to LES of global spectral method and direct numerical simulation. Overall, the results show that the spectral domain-decomposition grid-embedding technique provides an efficient method for resolving the near-wall region in LES of complex flows of engineering interest, allowing significant savings in the computational CPU and memory.

Spatial Variability of in situ and GOCI and MODIS Chlorophyll and CDOM in Summer at the East Sea (여름철 동해의 현장측정치와 GOCI와 MODIS 위성 자료로 측정한 엽록소와 유색용존유기물의 공간 변동성)

  • Park, Mi-Ok;Shin, Woo-Chul;Son, Young-Baek;Noh, Tae-Geun
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.21 no.4
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    • pp.327-338
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    • 2015
  • Because of impact on the underwater light field, CDOM can influence the accuracy of global satellite-based measurement of ocean chlorophyll and primary productivity. So we investigated the distribution and seasonal variation of CDOM in the East Sea during summer 2009 and 2011. Among them we report two distinctively different summer cases between 2009 and 2011 year, in which showed the different main sources for CDOM. Regulating factors and sources of CDOM in the East Sea were examined. Comparison between in situ and satellite derived Chl a and CDOM were made to find an influence of CDOM on measurement of satellite derived Chl a. Similar pattern and matching of MODIS Chl a with in situ Chl a 2009 was comparable, but significant discrepancy between MODIS Chl a and in situ Chl a was found, when CDOM was high in summer of 2011. GOCI data showed better matching with in situ data for both Chl a and CDOM, compared to MODIS data in summer of 2011. The presence of high CDOM at the surface layer supplied by vertical mixing seems to affect on the overestimation of Chl a by satellite data.

Detection of M:N corresponding class group pairs between two spatial datasets with agglomerative hierarchical clustering (응집 계층 군집화 기법을 이용한 이종 공간정보의 M:N 대응 클래스 군집 쌍 탐색)

  • Huh, Yong;Kim, Jung-Ok;Yu, Ki-Yun
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.30 no.2
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    • pp.125-134
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    • 2012
  • In this paper, we propose a method to analyze M:N corresponding relations in semantic matching, especially focusing on feature class matching. Similarities between any class pairs are measured by spatial objects which coexist in the class pairs, and corresponding classes are obtained by clustering with these pairwise similarities. We applied a graph embedding method, which constructs a global configuration of each class in a low-dimensional Euclidean space while preserving the above pairwise similarities, so that the distances between the embedded classes are proportional to the overall degree of similarity on the edge paths in the graph. Thus, the clustering problem could be solved by employing a general clustering algorithm with the embedded coordinates. We applied the proposed method to polygon object layers in a topographic map and land parcel categories in a cadastral map of Suwon area and evaluated the results. F-measures of the detected class pairs were analyzed to validate the results. And some class pairs which would not detected by analysis on nominal class names were detected by the proposed method.