Paper Info
Title
Mining text snippets for images on the web
Abstract
Images are often used to convey many different concepts or illustrate many different stories. We propose an algorithm to mine multiple diverse, relevant, and interesting text snippets for images on the web. Our algorithm scales to all images on the web. For each image, all webpages that contain it are considered. The top-K text snippet selection problem is posed as combinatorial subset selection with the goal of choosing an optimal set of snippets that maximizes a combination of relevancy, interestingness, and diversity. The relevancy and interestingness are scored by machine learned models. Our algorithm is run at scale on the entire image index of a major search engine resulting in the construction of a database of images with their corresponding text snippets. We validate the quality of the database through a large-scale comparative study. We showcase the utility of the database through two web-scale applications: (a) augmentation of images on the web as webpages are browsed and (b)~an image browsing experience (similar in spirit to web browsing) that is enabled by interconnecting semantically related images (which may not be visually related) through shared concepts in their corresponding text snippets.
Year
DOI
Venue
2014
10.1145/2623330.2623346
KDD
Keywords
Field
DocType
text snippets,browsing,text mining for images,interestingness,diversity,image databases,relevance,semantic image browsing,web image augmentation
Data mining,World Wide Web,Search engine,Web page,Information retrieval,Computer science,Web navigation,Snippet
Conference
Citations 
PageRank 
References 
4
0.45
21
Authors
12
Name
Order
Citations
PageRank
Anitha Kannan157046.43
Simon Baker27546543.98
Krishnan Ramnath3744.46
Juliet Fiss4211.13
Dahua Lin5111772.62
Lucy Vanderwende6105179.54
Rizwan Ansary740.45
Ashish Kapoor81833119.72
Qifa Ke9127357.06
Matt Uyttendaele10291.46
Xin-Jing Wang1159633.24
Lei Zhang1240.45