a-TRECVID: A dataset of images with multiple attributes
Summary
To facilitate multi-attribute based image retrieval [1] and
other attribute related research, we compile the largest multi-attribute image dateset to date, including 126 fully labeled attributes of
0.26 million images. This dataset is compiled from the TRECVID 2011
Semantic Indexing (SIN) track common annotation set by:
1. Discarding
attributes with too few positive images;
2. Discarding
images with too few local feature detection regions.
The original dataset includes about 0.3 million video frames
extracted from videos with durations ranging from 10s to just longer than 3.5
minutes. The total length of the videos is about 200 hours. Originally, there
are 346 fully labeled, unique query attributes for the video frames. The
attributes are mostly from the concepts defined in the LSCOM multimedia ontology. The annotation of
the dataset was organized by LIG (Laboratoire d'Informatique de
Grenoble) and LIF (Laboratoire d'Informatique Fondamentale de Marseille). Details can be found in their
website http://mrim.imag.fr/tvca/.
The
attributes
126 query attributes of a-TRECVID, selected from a pool of
346 concepts defined in TRECVID 2011 SIN task, by discarding attributes with too
few positive images.
Performance
evaluation using weak attributes [1][2]
Retrieval performance on a-TRECVID dataset, with the varying
training size. From left to right: performance of single, double and triple
attribute queries.
Download
Download the description for a-TRECVID here
(in Matlab format). You will need TRECVID 2011 SIN dataset in
order to get image data of a-TRECVID: http://www-nlpir.nist.gov/proj
You will also need to download the TRECVID 2011
collaborative annotation (*) http://mrim.imag.fr/tvca/ in
order to get the attribute labels. Please email [email protected] for
further information.
* S. Ayache and G. Quenot. Video corpus annotation using active
learning. In European Conference of Information Retrieval (ECIR), 2008
Project Page and Software Download
http://www.ee.columbia.edu/ln/dvmm/weak/
Publications
[1] Felix X. Yu; Rongrong Ji; Ming-Hen Tsai; Guangnan Ye; Shih-Fu Chang.
Weak attributes for
large-scale image retrieval, CVPR 2012 [PDF]
[Supplementary
Material]
[2] Felix
X. Yu; Rongrong Ji;
Ming-Hen Tsai; Guangnan Ye; Shih-Fu Chang.
Experiments of image
retrieval using weak attributes, Technical Report # CUCS 005-12 [PDF]