Sparse distributed localized gradient fused features of objects

Swathikiran Sudhakaran, Alex Pappachen James

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

The sparse, hierarchical, and modular processing of natural signals is related to the ability of humans to recognize objects with high accuracy. In this study, we report a sparse feature processing and encoding method, which improved the recognition performance of an automated object recognition system. Randomly distributed localized gradient enhanced features were selected before employing aggregate functions for representation, where we used a modular and hierarchical approach to detect the object features. These object features were combined with a minimum distance classifier, thereby obtaining object recognition system accuracies of 93% using the Amsterdam library of object images (ALOI) database, 92% using the Columbia object image library (COIL)-100 database, and 69% using the PASCAL visual object challenge 2007 database. The object recognition performance was shown to be robust to variations in noise, object scaling, and object shifts. Finally, a comparison with eight existing object recognition methods indicated that our new method improved the recognition accuracy by 10% with ALOI, 8% with the COIL-100 database, and 10% with the PASCAL visual object challenge 2007 database.

Original languageEnglish
Pages (from-to)1538-1546
Number of pages9
JournalPattern Recognition
Volume48
Issue number4
DOIs
Publication statusPublished - Apr 1 2015

Keywords

  • Brain-inspired system
  • Feature fusion
  • Hierarchy
  • Modularity
  • Object feature
  • Object recognition
  • Sparse feature

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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