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TagSense: Leveraging Smartphones for Automatic Image
Tagging
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 me rges them carefully
to create tags on-the-fly. The main challenge pe rtains 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.
Existing system
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.
Proposed system:
In this propose We envision TagSense, a mobile phone based collaborative system that
senses the people, activity, and context in a picture, and me rges them carefully to create
tags on-the-fly. The main challenge pe rtains to discriminating phone users that are in the

2. 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.
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
SOFTWARE CONFIGURATION:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.