A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Exploiting Multiple Web Resources towards Collecting Positive Training Samples for Visual Concept Learning
2015
Proceedings of the 5th ACM on International Conference on Multimedia Retrieval - ICMR '15
The number of images uploaded to the web is enormous and is rapidly increasing. The purpose of our work is to use these for acquiring positive training data for visual concept learning. Manually creating training data for visual concept classifiers is an expensive and time consuming task. We propose an approach which automatically collects positive training samples from the Web by constructing a multitude of text queries and retaining for each query only very few top-ranked images returned by
doi:10.1145/2671188.2749338
dblp:conf/mir/PapadopoulouM15
fatcat:wiyknuhhfnenvdb4kay5t4vf3m