Hilbert scan based bag-of-features for image retrieval

Pengyi Hao*, Sei Ichiro Kamata

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Generally, two problems of bag-of-features in image retrieval are still considered unsolved: one is that spatial information about descriptors is not employed well, which affects the accuracy of retrieval; the other is that the trade-off between vocabulary size and good precision, which decides the storage and retrieval performance. In this paper, we propose a novel approach called Hilbert scan based bag-of-features (HS-BoF) for image retrieval. Firstly, Hilbert scan based tree representation (HSBT) is studied, which is built based on the local descriptors while spatial relationships are added into the nodes by a novel grouping rule, resulting of a tree structure for each image. Further, we give two ways of codebook production based on HSBT: multi-layer codebook and multi-size codebook. Owing to the properties of Hilbert scanning and the merits of our grouping method, sub-regions of the tree are not only flexible to the distribution of local patches but also have hierarchical relations. Extensive experiments on caltech-256, 13-scene and 1 million ImageNet images show that HS-BoF obtains higher accuracy with less memory usage.

Original languageEnglish
Pages (from-to)1260-1268
Number of pages9
JournalIEICE Transactions on Information and Systems
Issue number6
Publication statusPublished - 2011 Jun


  • Bag-of-features
  • Feature representation
  • Hilbert scanning
  • Image search

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


Dive into the research topics of 'Hilbert scan based bag-of-features for image retrieval'. Together they form a unique fingerprint.

Cite this