Hilbert scan based tree representation for image search

Pengyi Hao*, Sei Ichiro Kamata

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


In this paper, Hilbert scan based tree representation (HSBT) is presented for image search. Unlike common ways decreasing the number of interest points or reducing the dimensions of features or using searching methods to match interest points, the proposed method builds a tree for each image and gives a new distance measure to calculate the similarity between the query and images in database. In the proposed approach, Hilbert scan for arbitrarily-sized arrays is used to map the interest points from two-dimensional space to one-dimensional space at first. Then, interest points set is divided into several parts by a separation way, and a grouping strategy is given to build a tree for each image. Experimental results show that the proposed approach is space saving. That is because it only stores clustering center and relevant information of each node in the tree. It is also time saving since the similarity calculation is up to the nodes of tree rather than all the descriptors of image. At the same time, the retrieval precision is good, because Hilbert scanning preserves the correlation in two-dimensional image, so nodes of tree are shaped according to the compactness of interest points which can employ the local information as much as possible.

Original languageEnglish
Title of host publicationTENCON 2010 - 2010 IEEE Region 10 Conference
Number of pages6
Publication statusPublished - 2010 Dec 1
Event2010 IEEE Region 10 Conference, TENCON 2010 - Fukuoka, Japan
Duration: 2010 Nov 212010 Nov 24

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON


Other2010 IEEE Region 10 Conference, TENCON 2010

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering


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