hellinger_vector_128_dsift(x, dtype=<type 'numpy.float32'>)¶
Computes a SIFT feature vector from a square patch (or image). Patch must be square and the output vector will always be a
(128,)vector. Please see
dsift()for more information.
The output of
vector_128_dsift()is normalized using the hellinger norm (also called the Bhattacharyya distance) which is a measure designed to quantify the similarity between two probability distributions. Since SIFT is a histogram based feature, this has been shown to improve performance. Please see  for more information.
- x (
Imageor subclass or
(C, Y, Y)ndarray) – Either the image object itself or an array with the pixels. The first dimension is interpreted as channels. Must be square i.e.
height == width.
- dtype (
np.dtype, optional) – The dtype of the returned vector.
ValueError– Only square images are supported.
 Arandjelovic, Relja, and Andrew Zisserman. “Three things everyone should know to improve object retrieval.”, CVPR, 2012.
- x (