Journal of KIISE:Computing Practices and Letters (한국정보과학회논문지:컴퓨팅의 실제 및 레터)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Image Identifier based on Local Feature's Histogram and Acceleration Technique using GPU

지역 특징 히스토그램 기반 영상식별자와 GPU 가속화

  • 전혁준 (충남대학교 컴퓨터공학부) ;
  • 서용석 (한국전자통신연구원 콘텐츠연구본부) ;
  • 황치정 (충남대학교 컴퓨터공학부)
  • Received : 2010.03.10
  • Accepted : 2010.07.07
  • Published : 2010.09.15
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Abstract

Recently, a cutting-edge large-scale image database system has demanded these attributes: search with alarming speed, performs with high accuracy, archives efficiently and much more. An image identifier (descriptor) is for measuring the similarity of two images which plays an important role in this system. The extraction method of an image identifier can be roughly classified into two methods: a local and global method. In this paper, the proposed image identifier, LFH(Local Feature's Histogram), is obtained by a histogram of robust and distinctive local descriptors (features) constrained by a district sub-division of a local region. Furthermore, LFH has not only the properties of a local and global descriptor, but also can perform calculations at a magnificent clip to determine distance with pinpoint accuracy. Additionally, we suggested a way to extract LFH via GPU (OpenGL and GLSL). In this experiment, we have compared the LFH with SIFT (local method) and EHD (global method) via storage capacity, extraction and retrieval time along with accuracy.

현대의 대량화된 영상 관리 시스템은 영상의 특징을 표현하는 영상식별자에 대해 왜곡에 강인하며 빠른 검색 속도, 정확성 및 효율적인 저장 등의 기본 성능을 요구한다. 영상식별자 설계 방법은 기하학적 왜곡에 강인한 지역 방식과 빠른 검색 및 적은 저장 용량의 속성을 지닌 전역방식으로 구분 할 수 있다. 본 논문에서는 왜곡에 강하고 지역적 공간적 제약으로 인한 서로간의 차별성이 강화된 지역 기술자들로부터 각각 개개 차원의 특징 분포도를 분석하여, 두 영상간의 유사도를 빠르고 정확하게 측정할 수 있는 지역 기술자 및 전역 기술자의 속성을 가지고 있는 LFH(Local Feature's Histogram)기반 영상식별자를 제안한다. 또한 GPU를 사용하여 LFH를 구현하는 방법을 제시하며, 제안한 LFH와 대표적인 지역, 전역 방식인 SIFT 및 EHD 방식과 저장용량, 추출 시간, 검색 속도 및 정확률에 대한 성능을 비교하였다.

Keywords

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