hellinger_vector_128_dsift¶
-
menpo.feature.
hellinger_vector_128_dsift
(x, dtype=<class 'numpy.float32'>)[source]¶ 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 seedsift()
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 1 for more information.- Parameters
x (
Image
or 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.
- Raises
ValueError – Only square images are supported.
References
- 1
Arandjelovic, Relja, and Andrew Zisserman. “Three things everyone should know to improve object retrieval.”, CVPR, 2012.