We were discussing Signature verification methods. We reviewed the stages involved with Signature verification. Then we also enumerated the feature extraction techniques. After that, we compared online and offline verification techniques. We also discussed the limitations of image processing and the adaptations for video processing.Then we proceeded to discussing image embedding in general.
We discussed Convolutional Neural Network (CNN) in image and object embedding in a shared space. Then we mentioned shape signature.
The purpose of the embedding is to map an image to a point in the embedding space so that it is close to a point attributed to a 3D model of a similar object. A large amount of training data consisting of images synthesized from 3D shapes is used to train the CNN.
Each shape is given a signature. This makes it easy to perform shape matching, organization and retrieval.  The shape signatures are computed using Light Field Descriptor. By re-using projections or views, the shape signature computation does not need any generalization.
Next we read about image retrieval in this framework.
The content-based image retrieval involves searching for an image that is similar to the query. Similarity can be low-level such as color and texture or high-level such as latent objects. In this scheme, both images and shapes are embedded into the same space.This space then leverages the robust distance metric for 3D shapes and trains a CNN for purifying and mapping real world images into this pre-built embedding space. Since the space is constructed already, there is a separation between that process and the image embedding process so that this becomes tractable. Moreover some synthesized images with annotations are used thereby eliminating the need for manual work in gaining annotations.

Object embedding in images used neural net. word embedding in documents used neural net  As such we don't yet have a technique that determines cohesive and adhesive properties other than vector space clustering. Is it possible to transform the embedding into a data mining problem ?

#codingexercise
How do you get the z-score in a normal probability distribution ?
double GetZScore(double X, double mu, Double sigma)
{
Debug.assert(sigma != 0);
return (X-mu)/sigma;
}

A step array is an array of integer where each element has a difference of atmost k with its neighbor. Given a key x, we need to find the index value of k if multiple element exist return the index of any  occurrence of key.
Input : arr[] = {4, 5, 6, 7, 6}
           k = 1
           x = 6
Output : 2

int GetIndex(List<int> A, int x, int k)
{
int i = 0;
while (i < A.Length)
{
if (A[i] == x)
   return i;
i = i + Math.Max(1, Math.Abs(A[i]-x)/k);
}
return -1;
}
If we were to return only the first element, we could scan the elements at positions before the index from where we exit the loop.