Self-Organizing Maps
The Earth Mover''s Distance as a Metric for Image Retrieval
The Earth Mover''s Distance as a Metric for Image Retrieval
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
PicSOM-self-organizing image retrieval with MPEG-7 content descriptors
IEEE Transactions on Neural Networks
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In this paper we study how the Self-Organizing Map (SOM) can be used in analysing the structure of semantic concepts in visual data. We investigate two data sets with concept labels provided by humans, and unlabelled data for which we utilise automatically detected concept membership scores by using models trained on a labelled data set. By arranging the concept memberships of visual objects as components of a vector, they can be used as the feature space for training a SOM. A visual and qualitative analysis of the SOM distributions of different concepts is augmented with a quantitative analysis based on measuring the Earth Mover's Distance between the vector distributions on the 2D SOM surface. In particular we study the PASCAL VOC 2007 and TRECVID 2010 databases, which are two large image and video data sets.