Normal form transformation for object recognition based on support vector machines

Shinsuke Sugaya, Einoshin Suzuki

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


This paper proposes Normal Form Transformation (NFT) as a preprocessing of Support Vector Machines (SVMs). Object recognition from images can be regarded as a fundamental technique in discovery science. Aspect-based recognition with SVMs is effective under constrained situations. However, object recognition from rotated, shifted, magnified or reduced images is a difficult task for simple SVMs. In order to circumvent this problem, we propose NFT, which rotates an image based on low-luminance directed vector and shifts, magnifies or reduces the image based on the object’s maximum horizontal distance and maximum vertical distance. We have applied SVMs with NFT to a database of 7200 images concerning 100 different objects. The recognition rates were over 97% in these experiments except for cases of extreme reduction. These results clearly demonstrate the effectiveness of the proposed approach in aspect-based recognition.

Original languageEnglish
Title of host publicationDiscovery Science - 2nd International Conference, DS 1999, Proceedings
EditorsSetsuo Arikawa, Koichi Furukawa
PublisherSpringer Verlag
Number of pages10
ISBN (Print)354066713X, 9783540667131
Publication statusPublished - 1999
Externally publishedYes
Event2nd International Conference on Discovery Science, DS 1999 - Tokyo, Japan
Duration: Dec 6 1999Dec 8 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other2nd International Conference on Discovery Science, DS 1999

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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