Demo of Content Based Image Retrieval, implemented by Python and Tornado.
- perceptual hash
- Otsu's method
- gray/RGB/YUV/HSV histograms
- GIST
- HoG and LSH (built by Kmeans clustering)
- SIFT and LSH (built by Kmeans clustering)
- Dense SIFT
- Hamming distance, or norm0 distance (L0)
- abs distance (L1)
- Eculidean distance (L2)
- blending: mix results
- ensembling: weighted sum
- util/: feature descriptors, feature and LSH preparation
- app/: http server, matching and retrieval
- templates/: html templates
- static/: datasets, js, css
- conf/: log.conf, and for feature data
- logs/: for log data
- settings.py: http port, common setting
- urls.py: server url path
- Tornado
- Image
- numpy, scipy
cd util/pyleargist-2.0.5/lear_gist/ && make && cp compute_gist ../../ && cd -cd util && python prepare.py && cd -python main.py- access http://localhost:19999/cbir
- add a new image folder in static/dataset/
- in util/prepare.py, change dataset to the folder name, like
dataset = 'ferrari' - run as previous section
Any question, please contact: Zuotao Liu([email protected])
