Create a searchable database¶
To create a Bag of Words database the following needs to be done
- Compute visual features (SIFT and color names)
- Compute a visual vocabulary for the features
- Create the Bag of Words database using the vocabularies
Visual features¶
The visual features used are SIFT and color names.
For this, two scripts are provided: vsearch_sift and vsearch_colornames.
SIFT¶
To compute SIFT features run:
vsearch_sift /path/to/images
This will create a number of XXXX.sift.h5 files where XXXX is the filename of an image, without the extension.
The descriptor file contains both SIFT descriptors and keypoints.
Color names¶
To compute color names features run:
vsearch_colornames /path/to/images
This will create a number of XXXX.cname.h5 files where XXXX is the filename of an image, without the extension.
If there is already an XXXX.sift.h5 file in the directory, those SIFT keypoints are used, otherwise new SIFT keypoints
are computed for this image (but not descriptors, and the XXXX.sift file is not created!)
Visual vocabulary¶
To create a visual vocabulary using SIFT features, with 50000 words run:
vsearch_vocabulary /path/to/images /path/to/output 50000 sift --iterations=15 --tries=100
To instead use color name features, replace sift with colornames.
This tool uses the K-means algorithm to cluster the data.
The --iterations flag tells how many K-means iterations to run. 15 should suffice in most cases.
The --tries flag controls the number of times the K-means algorithm is started, using a new randomly selected set of
seed points.
Larger vocabularies need a larger value.
Note that running K-means on a large dataset can take a lot of time!
The Database¶
Creating a visual database requires a directory of images that should be put into the database, and a vocabulary.
To create a database from SIFT features run:
vsearch_database /path/to/images /path/to/vocabulary /path/to/output sift
For a color names database, replace sift with colornames.