• DocumentCode
    3079988
  • Title

    Ontology-Based Image Retrieval with SIFT Features

  • Author

    Liu, Xuejun ; Shao, Zhenfeng ; Liu, Jun

  • Author_Institution
    Sch. of Remote Sensing & Inf. Eng., Wuhan Univ., Wuhan, China
  • fYear
    2010
  • fDate
    17-19 Sept. 2010
  • Firstpage
    464
  • Lastpage
    467
  • Abstract
    A new ontology-based image retrieval framework which brings in SIFT features is presented in this paper. Firstly, it brings SIFT features into the image ontology to build an ontology library which describes the image element together with shape, color, texture and other low-level features. And then, calculate the similarity of SIFT features, low-level features, concept of ontology and semantic features between the sample images and the image elements in the ontology library to retrieve images. The image semantic is taken into account in the framework which also using the SIFT features to maintain rotation invariance and scale invariance. Therefore, it can improve the accuracy of image retrieval.
  • Keywords
    feature extraction; gradient methods; image retrieval; image sampling; ontologies (artificial intelligence); SIFT feature; gradient vector; image element; image sampling; ontology based image retrieval; ontology library; rotation invariance; scale invariance; Feature extraction; Image color analysis; Image retrieval; Libraries; Ontologies; Semantics; Shape; SIFT; image retrieval gradient vector; ontology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pervasive Computing Signal Processing and Applications (PCSPA), 2010 First International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-1-4244-8043-2
  • Electronic_ISBN
    978-0-7695-4180-8
  • Type

    conf

  • DOI
    10.1109/PCSPA.2010.118
  • Filename
    5635489