I'm trying to perform a pixelwise multilabel based segmentation using the InfoGainLoss layer and have 2 classes which are relatively similar to each other. To now be able to discriminate between them, I would like to introduce negative distance values instead of positive similarity values at the corresponding positions in the infogain_matrix, so that the loss value for the pixels are proportional to P(true class) and antiproportional to P(similar class), ideally forcing a sharper discrimination between the classes.
However, afaik, in the current implementation of the InfoGainLoss, negative values in the infogain_mat would not result in the behaviour i want, but instead decrease the loss value by the absolute value of H(ln,k)*P(k). Anyone with more experience with the InfoGainLoss can confirm this? And if possible give suggestions on how to implement the behaviour I want?
Thanks a lot in advance!
I want to create a simple representation of an environment that basically just represents if at a certain position is an object or is not.
I would thus only need a big matrix filled with 1's and 0'. It is important to work effectively on this matrix, since I am going to have random positioned get and set operations on it, but also iterate over the whole matrix.
What would be the best solution for this?
My approach would be to create a vector of vectors containing bit elements. Otherwise, would there be an advantage of using a bitmap?
Note that while std::vector<bool> may consume less memory it is also slower than std::vector<char> (depending on the use case), because of all the bitwise operations. As with any optimization questions, there is only one answer: try different solutions and profile properly.
I'm currently studying lighting in OpenGL, which utilizes a function in GLSL called normalize. According to OpenGL docs, it says that it "calculates the normalized product of two vectors". However, it still doesn't explain what "normalized" mean. I have tried look for what a normalized product is on Google, however I can't seem to find anything about it. Can anyone explain what normalizing means and provide a few example of a normalized value?
I think the confusion comes from the idea of normalizing "a value" as opposed to "a vector"; if you just think of a single number as a value, normalization doesn't make any sense. Normalization is only useful when applied to a vector.
A vector is a sequence of numbers; in 3D graphics it is usually a coordinate expressed as v = <x,y,z>.
Every vector has a magnitude or length, which can be found using Pythagora's theorem: |v| = sqrt(x^2 + y^2 + z^2) This is basically the length of a line from the origin <0,0,0> to the point expressed by the vector.
A vector is normal if its length is 1. That's it!
To normalize a vector means to change it so that it points in the same direction (think of that line from the origin) but its length is one.
The main reason we use normal vectors is to represent a direction; for example, if you are modeling a light source that is an infinite distance away, you can't give precise coordinates for it. But you can indicate where to find it from a particular point by using a normal vector.
It's a mathematical term and this link explains its meaning in quite simple terms:
Operations in 2D and 3D computer graphics are often performed using copies of vectors that have been normalized ie. converted to unit vectors... Normalizing a vector involves two steps:
calculate its length, then,
divide each of its (xy or xyz) components by its length...
It's something complicated to explain if you don't know too much about vectors or even vectorial algebra. (You can check this article about general concepts as vector, normal vector or even normalization procedure ) Check it
But the procedure or concept of "normalize" refers to the process of making something standard or “normal.”
In the case of vectors, let’s assume for the moment that a standard vector has a length of 1. To normalize a vector, therefore, is to take a vector of any length and, keeping it pointing in the same direction, change its length to 1, turning it into what is called a unit vector.
I get a point in the 2-D plane (x,y) as input. Now I have to check which quadrant it is in, do some Reflections about X-axis and Y-axis and check again which quadrant it is in repeatedly for a large number of times.
I have two approaches but not sure which is better
I can store initially the x,y as boolean and then do boolean operations when Reflecting and hence this will be easy. To tell which quadrant the point is in just check the value is true or false.
Or I can do the normal approach with int and then check the first bit to find which quadrant the point is in
Neither, just compare your coordinates to 0.
If you store them as boolean, besides losing information, you'll probably get some overhead because of the conversion.
If you check the first bit, it will be less readable.
The compiler will optimize these calls on its own, doubt you'll gain anything from a different approach.
I'm looking for a data structure that would allow me to store an M-by-N 2D matrix of values contiguously in memory, such that the distance in memory between any two points approximates the Euclidean distance between those points in the matrix. That is, in a typical row-major representation as a one-dimensional array of M * N elements, the memory distance differs between adjacent cells in the same row (1) and adjacent cells in neighbouring rows (N).
I'd like a data structure that reduces or removes this difference. Really, the name of such a structure is sufficient—I can implement it myself. If answers happen to refer to libraries for this sort of thing, that's also acceptable, but they should be usable with C++.
I have an application that needs to perform fast image convolutions without hardware acceleration, and though I'm aware of the usual optimisation techniques for this sort of thing, I feel a specialised data structure or data ordering could improve performance.
Given the requirement that you want to store the values contiguously in memory, I'd strongly suggest you research space-filling curves, especially Hilbert curves.
To give a bit of context, such curves are sometimes used in database indexes to improve the locality of multidimensional range queries (e.g., "find all items with x/y coordinates in this rectangle"), thereby aiming to reduce the number of distinct pages accessed. A bit similar to the R-trees that have been suggested here already.
Either way, it looks that you're bound to an M*N array of values in memory, so the whole question is about how to arrange the values in that array, I figure. (Unless I misunderstood the question.)
So in fact, such orderings would probably still only change the characteristics of distance distribution.. average distance for any two randomly chosen points from the matrix should not change, so I have to agree with Oli there. Potential benefit depends largely on your specific use case, I suppose.
I would guess "no"! And if the answer happens to be "yes", then it's almost certainly so irregular that it'll be way slower for a convolution-type operation.
EDIT
To qualify my guess, take an example. Let's say we store a[0][0] first. We want a[k][0] and a[0][k] to be similar distances, and proportional to k, so we might choose to interleave the storage of first row and first column (i.e. a[0][0], a[1][0], a[0][1], a[2][0], a[0][2], etc.) But how do we now do the same for e.g. a[1][0]? All the locations near it in memory are now taken up by stuff that's near a[0][0].
Whilst there are other possibilities than my example, I'd wager that you always end up with this kind of problem.
EDIT
If your data is sparse, then there may be scope to do something clever (re Cubbi's suggestion of R-trees). However, it'll still require irregular access and pointer chasing, so will be significantly slower than straightforward convolution for any given number of points.
You might look at space-filling curves, in particular the Z-order curve, which (mostly) preserves spatial locality. It might be computationally expensive to look up indices, however.
If you are using this to try and improve cache performance, you might try a technique called "bricking", which is a little bit like one or two levels of the space filling curve. Essentially, you subdivide your matrix into nxn tiles, (where nxn fits neatly in your L1 cache). You can also store another level of tiles to fit into a higher level cache. The advantage this has over a space-filling curve is that indices can be fairly quick to compute. One reference is included in the paper here: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.30.8959
This sounds like something that could be helped by an R-tree. or one of its variants. There is nothing like that in the C++ Standard Library, but looks like there is an R-tree in the boost candidate library Boost.Geometry (not a part of boost yet). I'd take a look at that before writing my own.
It is not possible to "linearize" a 2D structure into an 1D structure and keep the relation of proximity unchanged in both directions. This is one of the fundamental topological properties of the world.
Having that that, it is true that the standard row-wise or column-wise storage order normally used for 2D array representation is not the best one when you need to preserve the proximity (as much as possible). You can get better result by using various discrete approximations of fractal curves (space-filling curves).
Z-order curve is a popular one for this application: http://en.wikipedia.org/wiki/Z-order_(curve)
Keep in mind though that regardless of which approach you use, there will always be elements that violate your distance requirement.
You could think of your 2D matrix as a big spiral, starting at the center and progressing to the outside. Unwind the spiral, and store the data in that order, and distance between addresses at least vaguely approximates Euclidean distance between the points they represent. While it won't be very exact, I'm pretty sure you can't do a whole lot better either. At the same time, I think even at very best, it's going to be of minimal help to your convolution code.
The answer is no. Think about it - memory is 1D. Your matrix is 2D. You want to squash that extra dimension in - with no loss? It's not going to happen.
What's more important is that once you get a certain distance away, it takes the same time to load into cache. If you have a cache miss, it doesn't matter if it's 100 away or 100000. Fundamentally, you cannot get more contiguous/better performance than a simple array, unless you want to get an LRU for your array.
I think you're forgetting that distance in computer memory is not accessed by a computer cpu operating on foot :) so the distance is pretty much irrelevant.
It's random access memory, so really you have to figure out what operations you need to do, and optimize the accesses for that.
You need to reconvert the addresses from memory space to the original array space to accomplish this. Also, you've stressed distance only, which may still cause you some problems (no direction)
If I have an array of R x C, and two cells at locations [r,c] and [c,r], the distance from some arbitrary point, say [0,0] is identical. And there's no way you're going to make one memory address hold two things, unless you've got one of those fancy new qubit machines.
However, you can take into account that in a row major array of R x C that each row is C * sizeof(yourdata) bytes long. Conversely, you can say that the original coordinates of any memory address within the bounds of the array are
r = (address / C)
c = (address % C)
so
r1 = (address1 / C)
r2 = (address2 / C)
c1 = (address1 % C)
c2 = (address2 % C)
dx = r1 - r2
dy = c1 - c2
dist = sqrt(dx^2 + dy^2)
(this is assuming you're using zero based arrays)
(crush all this together to make it run more optimally)
For a lot more ideas here, go look for any 2D image manipulation code that uses a calculated value called 'stride', which is basically an indicator that they're jumping back and forth between memory addresses and array addresses
This is not exactly related to closeness but might help. It certainly helps for minimation of disk accesses.
one way to get better "closness" is to tile the image. If your convolution kernel is less than the size of a tile you typical touch at most 4 tiles at worst. You can recursively tile in bigger sections so that localization improves. A Stokes-like (At least I thinks its Stokes) argument (or some calculus of variations ) can show that for rectangles the best (meaning for examination of arbitrary sub rectangles) shape is a smaller rectangle of the same aspect ratio.
Quick intuition - think about a square - if you tile the larger square with smaller squares the fact that a square encloses maximal area for a given perimeter means that square tiles have minimal boarder length. when you transform the large square I think you can show you should the transform the tile the same way. (might also be able to do a simple multivariate differentiation)
The classic example is zooming in on spy satellite data images and convolving it for enhancement. The extra computation to tile is really worth it if you keep the data around and you go back to it.
Its also really worth it for the different compression schemes such as cosine transforms. (That's why when you download an image it frequently comes up as it does in smaller and smaller squares until the final resolution is reached.
There are a lot of books on this area and they are helpful.