Mobile phones are becoming platforms for personal sensing, computing and communication. This paper proposes TagSense, a mobile phone system that uses sensing to automatically tag images. TagSense senses people, activities and contexts in pictures and merges them to create tags on-the-fly. It was tested on 8 Android phones with 200 pictures, showing it can effectively tag images. Compared to Apple and Google tagging systems, TagSense provides valuable tagging by using additional sensing dimensions, especially as devices and sensing algorithms improve.

1. GLOBALSOFT TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
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.