TagSense: a smartphone-based approach to automatic image tagging

  • Authors:

  • Chuan Qin;Xuan Bao;Romit Roy Choudhury;Srihari Nelakuditi

  • Affiliations:

  • University of South Carolina, Columbia, SC, USA;Duke University, Durham, NC, USA;Duke University, Durham, NC, USA;University of South Carolina, Columbia, SC, USA

  • Venue:
  • MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
  • Year:
  • 2011

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Abstract

Mobile phones are becoming the convergent platform for personal sensing, computing, and communication. This paper attempts to exploit this convergence towards the problem of automatic image tagging. We envision TagSense, a mobile phone based collaborative system that senses the people, activity, and context in a picture, and merges them carefully to create tags on-the-fly. The main challenge pertains to discriminating phone users that are in the picture from those that are not. We deploy a prototype of TagSense on 8 Android phones, and demonstrate its effectiveness through 200 pictures, taken in various social settings. While research in face recognition continues to improve image tagging, TagSense is an attempt to embrace additional dimensions of sensing towards this end goal. Performance comparison with Apple iPhoto and Google Picasa shows that such an out-of-band approach is valuable, especially with increasing device density and greater sophistication in sensing/learning algorithms.