Paper Info
Title
The MASH project
Abstract
It has been demonstrated repeatedly that combiningmultiple types of image features improves the performance of learning-based classification and regression. However, no tools exist to facilitate the creation of large pools of feature extractors by extended teams of contributors. The MASH project aims at creating such tools. It is organized around the development of a collaborative web platform where participants can contribute feature extractors, browse a repository of existing ones, run image classification and goal-planning experiments, and participate in public large-scale experiments and contests. The tools provided on the platform facilitate the analysis of experimental results. In particular, they rank the feature extractors according to their efficiency, and help to identify the failure mode of the prediction system.
Year
Venue
Keywords
2011
ECML/PKDD (3)
mash project,failure mode,image feature,combiningmultiple type,feature extractor,learning-based classification,collaborative web platform,extended team,image classification
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
32
Name
Order
Citations
PageRank
f abe100.34
william h allen200.34
t banks300.34
i a bond400.34
brian carter500.34
rhea j dodd600.34
masaki fujimoto700.34
n yayashida800.34
john b hearnshaw900.34
masanao honda1000.34
j jugaku1100.34
s kabe1200.34
masayoshi kobayashi1300.34
p m kilmartin1400.34
s kitamura1500.34
tracy e love1600.34
yukihiro matsubara1700.34
m mityamoto1800.34
yasushi muraki1900.34
takahiro nakamura2000.34
g s pennycook2100.34
leonie z pipe2200.34
m l reid2300.34
h sato2400.34
satoshi sato2500.34
to saito2600.34
maki sekiguchi2700.34
denis j sullivan2800.34
y watase2900.34
p c m yock3000.34
tatsuya yanagisawa3100.34
masayuki yoshizawa3200.34