Image annotation using search and mining technologies

ABSTRACT

In this paper, we present a novel solution to the image annotation problem which annotates images using search and data mining technologies. An accurate keyword is required to initialize this process, and then leveraging a large-scale image database, it 1) searches for semantically and visually similar images, 2) and mines annotations from them. A notable advantage of this approach is that it enables unlimited vocabulary, while it is not possible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.

REFERENCES

• 1: Jing Huang , S. Ravi Kumar , Mandar Mitra , Wei-Jing Zhu , Ramin Zabih, Image Indexing Using Color Correlograms, Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97), p.762, June 17-19, 1997
• 2: Wang, B., Li, Z.W., and Li, M.J. Efficient Duplicate Image Detection Algorithm for Web Images and Large-scale Database. In Technical Report of Microsoft Research, 2005
• 3: Hua-Jun Zeng , Qi-Cai He , Zheng Chen , Wei-Ying Ma , Jinwen Ma, Learning to cluster web search results, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1009030]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.8 Scene Analysis
          Subjects: Object recognition

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

General Terms:
Algorithms, Performance

Keywords:
hash indexing, image annotation, search result clustering