Title
Tagging Driven by Interactive Image Discovery: Tagging-Tracking-Learning
Abstract
With the exponential growth of web image data, image tagging is becoming crucial in many applications such as e-commerce. However, despite the great progress achieved in various image tagging technologies, none of them are able to incorporate browsing and discovery activities on web viewers in such a way that a user can easily query an image and ask the question "what is that in the image?". We have developed a comprehensive online image tagging system based on a Tagging-Tracking-Learning (TTL) framework to solve this problem. Tagging images using this system is able to turn common static web images into non-intrusive interactive images. The system tracks all browsing and interaction activities of users over time to filter out low quality tags and in turn helps the tagging process by alleviating manual operations. In this paper, we describe the implementation of the TTL framework and the novel algorithms developed. Usability studies of the system indicate that the TTL framework provides a better user experiences and simplifies the process of obtaining large tagged image collections over state-of-the-art approaches.
Year
DOI
Venue
2014
10.1109/ISM.2014.75
ISM
Keywords
Field
DocType
image tagging,ttl framework,web image data,web viewers,e-commerce,learning (artificial intelligence),nonintrusive interactive images,image processing,interactive image discovery,image retrieval,object tracking,internet,comprehensive online image tagging system,tagging-tracking-learning,image collections,image querying,games,image segmentation,computational modeling
Image map,Computer vision,Automatic image annotation,Information retrieval,Computer science,Image retrieval,Image processing,Digital image,Image segmentation,Artificial intelligence,Digital image processing,Tag system
Conference
Citations 
PageRank 
References 
1
0.35
16
Authors
4
Name
Order
Citations
PageRank
Qiong Wu1131.78
Rui Gao24314.55
Xida Chen3505.28
Boulanger, P.42810.62