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INRIA Holidays
Classification
Others
|...
License: Custom

Overview

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.

Statics

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

Data Format

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):

field field type description
x float horizontal position of the interest point
y float vertical position of the interest point
scale float scale of the interest region
angle float angle of the interest region
mi11 float affine matrix component
mi12 float affine matrix component
mi21 float affine matrix component
mi22 float affine matrix component
cornerness float saliency of the interest point
desdim int dimension of the descriptors
component byte*desdim the descriptor vector (dd components)
A matlab fileto read .siftgeo files.

.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.

field field type description
desdim int descriptor dimension
components float*desdim the centroids components

A matlab fileto read .fvecs files

Descriptor Extraction

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 versionof 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.jpgtmpfile=${infile/jpg/pgm}outfile=${infile/jpg/siftgeo}# Rescaling and intensity
normalizationdjpeg $infile | ppmtopgm | pnmnorm -bpercent=0.01 -wpercent=0.01 -maxexpand=400
| pamscale -pixels $[1024*768] > $tmpfile# Compute descriptorscompute_descriptors -i $tmpfile -o4
$outfile -hesaff -sift

The output format option -o4 produces a binary .siftgeo file, which
format is described above. The other available formats are described here.

NEW VERSION: the new version of the descriptor (pre-compiled).

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.

Citation

Please use the following citation when referencing the dataset:

@article{schmid2008hamming,
  title={Hamming Embedding and Weak Geometry Consistency for Large Scale Image Search--Extended
version--},
  author={Schmid, Herv{\'e} Jegou—Matthijs Douze—Cordelia},
  year={2008}
}

License

Custom

Data Summary
Type
Image,
Amount
1.491K
Size
3.92GB
Provided by
Inria
Inria is the national research institute in digital sciences and technologies.World-class research, technological innovation and entrepreneurial risk constitute its DNA.
| Amount 1.491K | Size 3.92GB
INRIA Holidays
Classification
Others
License: Custom

Overview

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.

Statics

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

Data Format

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):

field field type description
x float horizontal position of the interest point
y float vertical position of the interest point
scale float scale of the interest region
angle float angle of the interest region
mi11 float affine matrix component
mi12 float affine matrix component
mi21 float affine matrix component
mi22 float affine matrix component
cornerness float saliency of the interest point
desdim int dimension of the descriptors
component byte*desdim the descriptor vector (dd components)
A matlab fileto read .siftgeo files.

.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.

field field type description
desdim int descriptor dimension
components float*desdim the centroids components

A matlab fileto read .fvecs files

Descriptor Extraction

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 versionof 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.jpgtmpfile=${infile/jpg/pgm}outfile=${infile/jpg/siftgeo}# Rescaling and intensity
normalizationdjpeg $infile | ppmtopgm | pnmnorm -bpercent=0.01 -wpercent=0.01 -maxexpand=400
| pamscale -pixels $[1024*768] > $tmpfile# Compute descriptorscompute_descriptors -i $tmpfile -o4
$outfile -hesaff -sift

The output format option -o4 produces a binary .siftgeo file, which
format is described above. The other available formats are described here.

NEW VERSION: the new version of the descriptor (pre-compiled).

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.

Citation

Please use the following citation when referencing the dataset:

@article{schmid2008hamming,
  title={Hamming Embedding and Weak Geometry Consistency for Large Scale Image Search--Extended
version--},
  author={Schmid, Herv{\'e} Jegou—Matthijs Douze—Cordelia},
  year={2008}
}

License

Custom

0
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