Just how to assess the similarity between two pictures?
We have two team pictures for pet and dog. And every team have 2000 pictures for pet and dog correspondingly.
My objective is attempt to cluster the pictures by utilizing k-means.
Assume image1 is x , and image2 is y .Here we have to assess the similarity between any two images. what’s the typical option to determine between two pictures?
1 Response 1
Well, there a couple of therefore. lets go:
A – utilized in template matching:
Template Matching is linear and it is maybe not invariant to rotation (really not really robust to it) however it is pretty simple and easy robust to sound including the ones in photography taken with low lighting.
It is possible to implement these OpenCV Template that is using Matching. Bellow there are mathematical equations defining a number of the similarity measures (adapted for comparing 2 equal sized pictures) employed by cv2.matchTemplate:
1 – Sum Square Huge Difference
2 – Cross-Correlation
B – visual descriptors/feature detectors:
Numerous descriptors had been developed for pictures, their use that is main is register images/objects and seek out them various other scenes. But, nevertheless they feature a large amount of information regarding the image and were utilized in student detection (A joint cascaded framework for simultaneous attention detection and attention state estimation) as well as seem it utilized for lip reading (can not direct one to it since I’m not certain it was currently posted)
They detect points which can be regarded as features in pictures (appropriate points) the neighborhood texture of the points as well as their geometrical place to one another may be used as features.
You can easily find out more if you want to keep research on Computer vision I recomend you check the whole course and maybe Rich Radke classes on Digital Image Processing and Computer Vision for Visual Effects, there is a lot of information there that can be useful for this hard working computer vision style you’re trying to take about it in Stanford’s Image Processing Classes (check handouts for classes 12,13 and 14)
1 – SIFT and SURF:
They are Scale Invariant techniques, SURF is a speed-up and available type of SIFT, SIFT is proprietary.
2 – BRIEF, BRISK and FAST:
They are binary descriptors and they are really quick (primarily on processors by having a pop_count instruction) and that can be used in a way that is similar SIFT and SURF. Also, i have used BRIEF features as substitutes on template matching for Facial Landmark Detection with a high gain on rate with no loss on precision for the IPD additionally the KIPD classifiers, although i did not publish any one of it yet (and also this is simply an incremental observation regarding the future articles and so I do not think there is certainly harm in sharing).
3 – Histogram of Oriented Gradients (HoG):
That is rotation invariant and it is employed for face detection.
C – Convolutional Neural Systems:
I’m sure that you don’t desire to utilized NN’s but i do believe it really is fair to point they have been REALLY POWERFULL, training a CNN with Triplet Loss could be very nice for learning a representative function area for clustering (and category).
Check always Wesley’s GitHub 
for a typical example of it is energy in facial recognition making use of Triplet Loss to get features after which SVM to classify.
Additionally, if Deep Learning to your problem is computational price, it is possible to find pre-trained levels with dogs and cats around.
D – check up on previous work:
This dogs and cats battle happens to be taking place for a long time. you should check solutions on Kaggle Competitions (Forum and Kernels), there have been 2 on dogs and cats that one and That One
E – Famous Measures:
- SSIM Structural similarity Index
- L2 Norm ( Or Euclidean Distance)
- Mahalanobis Distance
F – check into other type of features
Dogs and cats could be a simple to determine by their ears and nose. size too but I’d kitties as large as dogs.
so not really that safe to utilize size.
You could take to segmenting the images into animals and history and try to do then area home analisys.
When you yourself have enough time, this guide right here: Feature Extraction & Image Processing for Computer Vision from Mark S. Nixon have much information on this type of procedure
You can look at Fisher Discriminant review and PCA to generate a mapping together with evaluate with Mahalanobis Distance or L2 Norm
