From aardvark to zorro: A benchmark of mammal images

From aardvark to zorro: A benchmark of mammal images (2007)

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by Michael Fink , Shimon Ullman

Venue:	In IJCV (Submitted
Citations:	10 - 1 self

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Datum	Value	Source
TITLE	From aardvark to zorro: A benchmark of mammal images	INFERENCE
AUTHOR NAME	Michael Fink	SVM HeaderParse 0.2
AUTHOR AFFIL	Interdisciplinary Center for Neural Computation; The Hebrew University of Jerusalem	SVM HeaderParse 0.2
AUTHOR ADDR	Jerusalem 91904, Israel	SVM HeaderParse 0.2
AUTHOR NAME	Shimon Ullman	SVM HeaderParse 0.2
AUTHOR AFFIL	Faculty of Mathematics and Computer Science; Weizmann Institute of Science	SVM HeaderParse 0.2
AUTHOR ADDR	Rehovot 76100, Israel	SVM HeaderParse 0.2
ABSTRACT	Current object recognition systems aim at achieving two challenging goals: recognizing numerous object classes and learning new object classes from a small number of examples. This paper provides a benchmark for evaluating progress on these fundamental tasks. Several methods have recently proposed to utilize the commonalities between object classes in order to effectively acquire new object classes. Such methods can be termed interclass transfer techniques. However, it is currently difficult to asses which of the proposed methods maximally utilizes the shared structure of related classes. To facilitate the development, as well as the assessment of methods for dealing with multiple related classes, the proposed benchmark provides labeled images of over ¢¡¢¡ mammal classes. The images are organized in five levels of variability, and their labels provide information on the objects identity, location and pose. A labeled benchmark containing a large number of related classes is crucial for assessing how well proposed solutions scale up, and which methods better utilize interclass transfer. 1	SVM HeaderParse 0.2
YEAR	2007	INFERENCE
VENUE	In IJCV (Submitted	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	0 found	ParsCit 1.0