I want to find how much information an image has, for example if it contains human or other objects, then it'll have more information and if an image doesn't contain such objects, it indicates the low-level of information present inside it.
Previously I've experience with entropy and complexity measure already, and similarly I also have pretty experience with saliency based techniques.
If anyone know about some other low-level features based measure, he should suggest me here. Finally, if someone share information about how can we represent saliency of an image through a real-value, it'd be highly appreciated.
Entropy will not give your target information. Rather, you have to implement the object detection algorithm first. First try to detect the predefined set of objects like human, chair, bed, etc. Weight each one with a value. Then follow one of the following:
1) Calculate the total weight in the image regarding object.
2) Calculate sum of weights of pixels. In that case, apply the weight of pixels according to their belonging to the objects.
Next apply that weight in another equation to compute your result according to the demand of your research.
An algorithm must work
First calculate the number of pixels in the image
Tricky question.
You need a set of reference images considered informative, and others considered informationless. Then you define a distance between images, and you allocate a threshold for your classification...
The so-called "entropy " doesn't exist at all.
During the process of deriving the so-called entropy, in fact, ΔQ/T can not be turned into dQ/T. That is, the so-called "entropy " doesn't exist at all.
The so-called entropy was such a concept that was derived by mistake in history.
It is well known that calculus has a definition,
any theory should follow the same principle of calculus; thermodynamics, of course, is no exception, for there's no other calculus at all, this is common sense.
Based on the definition of calculus, we know:
to the definite integral ∫T f(T)dQ, only when Q=F(T), ∫T f(T)dQ=∫T f(T)dF(T) is meaningful.
As long as Q is not a single-valued function of T, namely, Q=F( T, X, …), then,
∫T f(T)dQ=∫T f(T)dF(T, X, …) is meaningless.
1) Now, on the one hand, we all know that Q is not a single-valued function of T, this alone is enough to determine that the definite integral ∫T f(T)dQ=∫T 1/TdQ is meaningless.
2) On the other hand, In fact, Q=f(P, V, T), then
∫T 1/TdQ = ∫T 1/Tdf(T, V, P)= ∫T dF(T, V, P) is certainly meaningless. ( in ∫T , T is subscript ).
We know that dQ/T is used for the definite integral ∫T 1/TdQ, while ∫T 1/TdQ is meaningless, so, ΔQ/T can not be turned into dQ/T at all.
that is, the so-called "entropy " doesn't exist at all.
.
.
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Why did the wrong "entropy" appear ?
In summary , this was due to the following two reasons:
1) Physically, people didn't know Q=f(P, V, T).
2) Mathematically, people didn't know AΔB couldn‘t become AdB directely .
If people knew any one of them, the mistake of entropy would not happen in history.
Please read my paper and those answers of the questions related to my paper in my Projects.
https://www.researchgate.net/publication/230554936_Entropy_A_concept_that_is_not_a_physical_quantity
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