Name
Abstract | ||
|---|---|---|
In this paper we look at how images can be labelled as a result of click throughs from searches. One approach acts as a filter on image searches specifically, while the other approach propagates labels to images from their containing pages, where those pages were labelled themselves using clickthrough as a filter on text search. Then the paper reports on an experiment where users ranked for relevance six methods for labelling images, comparing the two clickthrough-based methods with flickr's amateur explicit labelling, Getty's professional explicit labelling, Google's standard image search, and the new Google Image Labeller. As well as comparing the accuracy of the proposed image labelling methods and discovering that automatic methods outperform explicit human labelling methods, the experiment suggests clickthrough data is reliable with very few clicks for image classification purposes. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/WI-IAT.2009.35 | Web Intelligence |
Keywords | Field | DocType |
information science,search engines,intelligent agent,image classification,labeling | Data mining,Intelligent agent,Search engine,Information retrieval,Ranking,Computer science,Full text search,Information science,Amateur,Labelling,Contextual image classification | Conference |
Volume | Citations | PageRank |
1 | 6 | 0.45 |
References | Authors | |
18 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helen Ashman | 1 | 767 | 66.74 |
| Michael Antunovic | 2 | 15 | 1.96 |
| Christoph Donner | 3 | 6 | 0.45 |
| Rebecca Frith | 4 | 6 | 0.45 |
| Eric Rebelos | 5 | 6 | 0.45 |
| Jan-Felix Schmakeit | 6 | 9 | 1.20 |
| Gavin Smith | 7 | 43 | 5.31 |
| Mark Truran | 8 | 286 | 14.43 |