[Solomonov Seminar] [E8] 216. Solomonov seminar

[Marko Grobelnik]

V torek 16. marca bo ob 13:00h v Veliki predavalnici IJS
(drugo nadstropje glavne zgradbe), 217. Solomonov seminar.
Posnetki preteklih seminarjev so na http://videolectures.net/solomon/

Predaval bo Ivica Dimitrovski o analizi in anotaciji digitalnih fotografij
z ansambli odlocitvenih dreves

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Ivica Dimitrovski:
    Automatic Annotation of Images using Ensembles of Trees for 
Hierarchical Multi-label Classification

This research presents a large scale system for detection of visual 
concepts and annotation of images. The system is composed of two parts: 
feature extraction and classification/ annotation. The feature 
extraction part provides global and local descriptions of the images in 
the form of numerical vectors. Using these numerical descriptions, we 
train a classifier, a predictive clustering tree (PCT), to produce 
annotations for unseen images. PCTs are able to handle target concepts 
that are organized in a hierarchy, i.e., perform hierarchical 
multi-label classification. To improve the classification performance, 
we construct ensembles (bags and random forests) of PCTs.


We evaluate our system on two different databases: IRMA database which 
contains medical images and the image database from the ImageCLEF at ICPR 
2010 photo annotation task which contains general images. The extensive 
experiments conducted on the benchmark databases show that our system 
has very high predictive performance and can be easily scaled to large 
amounts of visual concepts and data. In addition, our approach is very 
general: it can be easily extended with new feature extraction methods, 
and it can thus be easily applied to other domains, types of images and 
other classification schemes. Furthermore, it can handle arbitrarily 
sized hierarchies organized as trees or directed acyclic graphs.