cv.ImgHash - mexopencv

Base class for Image Hashing algorithms

This module brings implementations of different image hashing algorithms. It provides algorithms to extract the hash of images and fast way to figure out most similar images in huge data set.

Supported Algorithms

Average hash (also called Different hash)
PHash (also called Perceptual hash)
Marr Hildreth Hash
Radial Variance Hash
Block Mean Hash (modes 0 and 1)
Color Moment Hash (this is the one and only hash algorithm resist to rotation attack (-90~90 degree)

You can study more about image hashing from the paper and websites listed in the references section.

Performance under different attacks

Performance chart

Speed comparison with PHash library (100 images from ukbench)

Hash Computation chart Hash comparison chart

As you can see, hash computation speed of img_hash module outperform PHash library a lot.

PS: I do not list out the comparison of Average hash, PHash and Color Moment hash, because I cannot find them in PHash.

Motivation

Collects useful image hash algorithms into OpenCV, so we do not need to rewrite them by ourselves again and again or rely on another 3rd party library (ex: PHash library). BOVW or correlation matching are good and robust, but they are very slow compare with image hash, if you need to deal with large scale CBIR (content based image retrieval) problem, image hash is a more reasonable solution.

More info

You can learn more about img_hash modules from following links, these links show you how to find similar image from ukbench dataset, provide thorough benchmark of different attacks (contrast, blur, noise (gaussion, pepper and salt), jpeg compression, watermark, resize).

Contributors

Tham Ngap Wei

References

[lookslikeit]:

Neal Krawetz. Looks Like It.

[tang2012perceptual]:

Zhenjun Tang, Yumin Dai, Xianquan Zhang. "Perceptual Hashing for Color Images Using Invariant Moments. Appl. Math, 6(2S):643S-650S, 2012.

[zauner2010implementation]:

Christoph Zauner. "Implementation and benchmarking of perceptual image hash functions". 2010.

Superclasses	handle
Sealed	false
Construct on load	false

	addlistener	Add listener for event.
Static	averageHash	Calculates the average hash in one call
Static	blockMeanHash	Computes block mean hash of the input image
Static	colorMomentHash	Computes color moment hash of the input
	compare	Compare two hash values
	compute	Computes hash of the input image
	delete	Destructor
	eq	== (EQ) Test handle equality.
	findobj	Find objects matching specified conditions.
	findprop	Find property of MATLAB handle object.
	ge	>= (GE) Greater than or equal relation for handles.
	gt	> (GT) Greater than relation for handles.
Sealed	isvalid	Test handle validity.
	le	<= (LE) Less than or equal relation for handles.
	listener	Add listener for event without binding the listener to the source object.
	lt	< (LT) Less than relation for handles.
Static	marrHildrethHash	Computes average hash value of the input image
	ne	~= (NE) Not equal relation for handles.
	notify	Notify listeners of event.
Static	pHash	Computes pHash value of the input image
Static	radialVarianceHash	Computes radial variance hash of the input image
	typeid	Name of the C++ type (RTTI)