a-TRECVID: A dataset of images with multiple attributes

Summary

To facilitate multi-attribute based image retrieval 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.

This dataset can be considered as a large multi-label image dataset.

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

Performance evaluation using weak attributes

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).

NEW: Download the features and labels:

trecvid_inf_with_attribute_label.mat (query attribute groud truth, training split, 123MB)
Images are from TRECVID 2011 SIN dataset
http://www-nlpir.nist.gov/projects/tv2011/tv2011.html#sin.
Drop me a message for the images and more feature files.
yuxinnan (at) ee.columbia.edu

Related Resources:
TRECVID 2011 SIN dataset: http://www-nlpir.nist.gov/projects/tv2011/tv2011.html#sin.
TRECVID 2011 collaborative annotation (*): http://mrim.imag.fr/tvca/

* 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/~yuxinnan/weak.html

Publications

Felix X. Yu; Rongrong Ji; Ming-Hen Tsai; Guangnan Ye; Shih-Fu Chang.
Weak attributes for large-scale image retrieval
CVPR 2012 [PDF]

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]