Selective Search in Python
This is a full implementation of selective search in Python. The implementation is typically based on this paper. It have three selective search modes according to various diversification strategies as in the paper.
Installing from PyPI is recommended :
$ pip install selective-search
It is also possible to install the latest version from Github source:
$ git clone https://github.com/ChenjieXu/selective_search.git $ cd selective_search $ python setup.py install
import skimage.io from selective_search import selective_search # Load image as NumPy array from image files image = skimage.io.imread('path/to/image') # Run selective search using single mode boxes = selective_search(image, mode='single', random=False)
For detailed examples, refer this part of the repository.
Three modes correspond to various combinations of diversification strategies. The appoach to combine different diversification strategies, say, color spaces, similarity measures, starting regions is listed in the following table.
|Mode||Color Spaces||Similarity Measures||Starting Regions (k)||Number of Combinations|
|fast||HSV, Lab||CTSF, TSF||50, 100||8|
|quality||HSV, Lab, rgI, H, I||CTSF, TSF, F, S||50, 100, 150, 300||80|
Color Space [Source Code]
Initial oversegmentation algorithm and our subsequent grouping algorithm are performed in this colour space.
Similarity Measure [Source Code]
'CTSF' means the similarity measure is aggregate of color similarity, texture similarity, size similarity, and fill similarity.
Starting Region [Source Code]
A parameter of initial grouping algorithm, which yields high quality starting locations efficiently. A larger k causes a preference for larger components of initial strating regions.
If random set to True, function will carry out pseudo random sorting. It only alters sequences of bounding boxes, instead of locations, which prevents heavily emphasis on large regions as combing proposals from up to 80 different strategies. This only has a significant impact when selecting a subset of region proposals with high rankings, as in RCNN.
 J. R. R. Uijlings et al., Selective Search for Object Recognition, IJCV, 2013
 Felzenszwalb, P. F. et al., Efficient Graph-based Image Segmentation, IJCV, 2004
 Segmentation as Selective Search for Object Recognition
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