A hardware accelerated multilevel visual classifier for embedded visual-Assist systems

Matthew Cotter, Siddharth Advani, John Morgan Sampson, Kevin Irick, Vijaykrishnan Narayanan

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


Embedded visual assist systems are emerging as increasingly viable tools for aiding visually impaired persons in their day-To-day life activities. Novel wearable devices with imaging capabilities will be uniquely positioned to assist visually impaired in activities such as grocery shopping. However, supporting such time-sensitive applications on embedded platforms requires an intelligent trade-off between accuracy and computational efficiency. In order to maximize their utility in real-world scenarios, visual classifiers often need to recognize objects within large sets of object classes that are both diverse and deep. In a grocery market, simultaneously recognizing the appearance of people, shopping carts, and pasta is an example of a common diverse object classification task. Moreover, a useful visual-Aid system would need deep classification capability to distinguish among the many styles and brands of pasta to direct attention to a particular box. Exemplar Support Vector Machines (ESVMs) provide a means of achieving this specificity, but are resource intensive as computation increases rapidly with the number of classes to be recognized. To maintain scalability without sacrificing accuracy, we examine the use of a biologically-inspired classifier (HMAX) as a front-end filter that can narrow the set of ESVMs to be evaluated. We show that a hierarchical classifier combining HMAX and ESVM performs better than either of the two individually. We achieve 12% improvement in accuracy over HMAX and 4% improvement over ESVM while reducing computational overhead of evaluating all possible exemplars.

Original languageEnglish (US)
Article number7001338
Pages (from-to)96-100
Number of pages5
JournalIEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Issue numberJanuary
StatePublished - Jan 5 2015
Event2014 33rd IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2014 - San Jose, United States
Duration: Nov 2 2014Nov 6 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design


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