INRIA is the copyright holder of all the images included in the dataset.
If you use this dataset, please cite the following paper:
Herve Jegou, Matthijs Douze and Cordelia Schmid
"Hamming Embedding and Weak geometry consistency for large scale image search"
Proceedings of the 10th European conference on Computer vision, October, 2008
This data set is provided "as is" and without any express or implied
warranties, including, without limitation, the implied warranties of
merchantability and fitness for a particular purpose.
Dataset description
The Holidays dataset is a set of images which mainly contains some of
our personal holidays photos. The remaining ones were taken on purpose
to test the robustness to various attacks: rotations, viewpoint and
illumination changes, blurring, etc. The dataset includes a very large
variety of scene types (natural, man-made, water and fire effects,
etc) and images are in high resolution. The dataset contains 500 image
groups, each of which represents a distinct scene or object. The first
image of each group is the query image and the correct retrieval
results are the other images of the group.
The dataset can be downloaded from this page, see details below.
The material given includes:
the images themselves
the set of descriptors extracted from these images (see details below)
a set of descriptors produced, with the same extractor and descriptor,
for a distinct dataset (Flickr60K).
two sets of clusters used to quantize the descriptors. These have been
obtained from Flickr60K.
some pre-processed feature files for one million images, that we have used
in our ECCV paper to perform the evaluation on a large scale.
In our paper, we
also used some sets of distractor images downloaded from Flickr.
Their features are provided below.
Download and Statistics
Dataset size: 1491 images in total: 500 queries and 991 corresponding relevant images
Number of queries: 500 (one per group)
Number of descriptors produced: 4455091 SIFT descriptors of dimensionality 128
Download
pre-computed features for one million images, stored in 1000 archives of 1000 feature files each (235GB in total).
Two binary file formats are used.
.siftgeo format
descriptors are stored in raw together with the region information
provided by the software of Krystian Mikolajczyk.
There is no header (use the file length to find
the number of descriptors).
A descriptor takes 168 bytes (floats and ints take 4 bytes, and are stored in little endian):
.fvecs format
This one is used to store centroids. As for the .siftgeo format, there is no header.
Centroids are stored in raw. Each centroid takes 516 bytes, as shown below.
Before computing descriptors, we have resized the images to a
maximum of 786432 pixels and performed a slight intensity
normalization.
For the descriptor extraction, we have used a modified
version
of the software of
Krystian Mikolajczyk
(thank you Krystian!).
We have used the Hessian-Affine extractor and the SIFT descriptor.
Note however that
our version of the code may be different from the one which is currently on the web.
If so, this should not noticeably impact the results.
The set of commands used to extract the descriptors was the following.
Note that we have used the default values for descriptor generation.
infile=xxxx.jpg
tmpfile=${infile/jpg/pgm}
outfile=${infile/jpg/siftgeo}
It is almost the same as the one above, but it includes dense sampling as well.
Also, it does not depend on ImageMagick anymore, for improved portability.
As a result, input JPG format is no longer supported.
For the same set of parameters, there might be some small differences between
the output of this version of the previous one, but there differences are mainly precision ones
and the output of the two softwares are intended to be compatible.
Evaluation protocol
We have used to following protocol to evaluate our image search system.
the performance is measured by the mean average precision
averaged over all 500 queries. Our evaluation package
is inspired from the software of Oxford.
the query images are not counted as true positives when
computing the mAP (of course, not as false positive). They are assumed to be "junk" images,
as defined in the evaluation software of Oxford.
we have learned all our parameters on a distinct dataset
(clustering, etc) to better reflect the accuracy of a real system
(where the relevant images are a small fraction of the overall image
dataset)
Some papers reporting some results on Holidays
Matthijs Douze has collected a few results achieved on INRIA Holidays, mostly collected in Major computer vision conferences (This list is not exhaustive):
Results on Holidays.
Copydays dataset
Copyright Notice
INRIA is the copyright holder of all the images included in the dataset.
This data set is provided "as is" and without any express or implied
warranties, including, without limitation, the implied warranties of
merchantability and fitness for a particular purpose.
Dataset description
The Copydays dataset is a set of images which is exclusively composed of
our personal holidays photos.
Each image has suffered three kinds of artificial attacks: JPEG, cropping and "strong".
The motivation is to evaluate the behavior of indexing algorithms for most common image copies
(for video, you may be interest in our video copy generation software).
Because of its small size,
it is evaluated by merging the original images in a large image dabase.
The dataset can be downloaded from this page, see details below.
The material given includes the images themselves. On request we may also provide
the set of descriptors extracted from these images. For the evaluation,
one should ideally use the same sets of distractor images downloaded from Flickr
than we used. We can provide them on request.
You may be interested in downloading some material provided for the Holidays dataset: the visual vocabularies, the set of features extracted from an independent dataset, some pre-processed features for one million images, the software used to compute the features, the extraction procedure and, finally, the evaluation protocol.