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Correlation between the inproduct and the sum of absolute differences is -0.8485 for uniform sampled signals on [-1:1]
Werner Van Belle1* - email@example.com, firstname.lastname@example.org
Abstract : While investigating relations between various comparators and the inproduct we found that the inproduct correlates strongly towards the absolute difference when the domain from which the values are taken come from a uniform distribution on [-1:1]. This useful relation might help to speed up block comparison in content databases and can be used as a valuable tool to estimate the inproduct based on the absolute difference and vice versa
sum of absolute differences, sum of inproduct, landmark tracking, feature tracking
To compare two data-blocks (of size ), two easy implementable techniques are widely used. The first is the summed inproduct (SIP), defined as . The second one, termed the sum of absolute differences (SAD), is defined as . Depending on the application behind the comparison one might be favored over the other.
The absolute difference is a well known operator for block comparison within images and is one of the basic blocks used in MPEG coding . It is often used for motion estimation by means of landmark detection [2, 3, 4, 5] and many different hardware implementations of the absolute difference exist [6, 7, 8, 9]. In general statistics, the absolute difference is also often used as a robust measure , with specialized application such as model evaluation of protein 3D structures .
In audio applications, the absolute difference could also be used, but for historical reasons, the standard inproduct seems to be favored, mainly because multiplication is a basic operation in digital filters . The inproduct, and specifically convolution, has an interesting application in fuzzy pattern matching. There it is often used to find sub-patterns in a haystack of information. Convolution will then assess the correlation between every position and the pattern-to-be-found [13, 14, 15]. Since convolution itself can be efficiently implemented through a Fourier transform (or a sliding sliding window Fourier transform when the datasets are large), pattern matching becomes a faster operation than finding similar fragment by means of the absolute difference. To go somewhat into the details, we denote the Fourier transform as . On a block of size it takes time complexity log . The inproduct of two signals expressed in their frequency domain is the same as convolution of their information in the time domain . As such, the function , in which denotes the inproduct and denotes conjugation, will return a vector of which element describes the correlation between signal and signal shifted positions. This operation itself has time complexity , which is much faster than the same process using an iteration of absolute differences, which would yield a time complexity of .
As such, the two operations both have their advantages and disadvantages. The absolute difference can be quickly calculated, but it is difficult to optimize for many comparisons. The inproduct on the other hand is slower on individual comparisons, but has very efficient implementations for large volumes.
Aside from these trade-offs, both metrics behave very different. The sum of absolute differences will be small for similar blocks, while the inproduct instead will be large. Some other subtitles arise as well: the absolute difference is independent of any constant added to both vectors since . The inproduct does not have such a property: and will provide different results for any two same values: when . In other words the strength of the reported similarity depends on the strength of the values themselves. Actually, the inproduct itself is strictly speaking not a metric since for most values of .
Notwithstanding their completely different mathematical behavior, both are often used in very similar contexts and therefore, we became interested to understand the relation between a number of well known comparators such as the summed inproduct and the sum of absolute differences. This could make it possible to predict one in function of the other.
To study the relations between various comparators we created data-blocks, each containing two vectors and . The variables and , both of size and element from , were randomly sampled from the same multi-variate distribution. Based on such test vectors we measured the spearman rank order and linear pearson correlation. and annotate the elements of and , with the variables and . americanFor 8-bit signed integers, and ranges within [-127,128], and for 16 bit signed integers the values come from [-32767,32768]. americanWe do not take into account the discrete nature of integers and will work with real numbers taken from . americanAll and values were drawn from the same distribution, which could be normal or uniform distributed.
The first operator is the absolute difference. The second operator is the multiplication. The third operator will provide us with insight on the relation between the absolute difference and the basic difference. The fourth operator investigates the impact of signed to unsigned conversion in a comparison process and the last operator. takes the inverse of the signal and puts it back into the equation as an attempt to balance the inequality that occurs when comparing identical couples with a different value.
The multidimensional comparator , based on is defined as
We tested the relations between every pair of comparators using
sequences each of
samples. The values for
were first drawn from a uniform distribution between and
and then from a normal distribution. The random generators are ran2
and gasdev from .
In all cases, both
the linear Pearson and spearman rank order correlations were similar.
The programs to measure the correlations can be found in section
We now assume that both vectors and are uniform distributed within range , with and . denotes the size of the interval ( ). The comparators and , both depending on and , will now be considered to be variables themselves. The correlation between them, , is given by
Under the assumption that the various sample positions are independent from each other, one can easily show that .
This reduces the 5 estimates we need to , , , and . We will now determine those 5 values.
Estimates of a variable can be calculated from probability functions as follows ( is the probability distribution function of the variable )
To calculate the probability distribution function of with both and uniform distributions over , we must first obtain the probability function of , which is given by :
Since this probability distribution is symmetrical around (the means of the two uniform distribution has been subtracted), we would get an expected value of 0, but then we would forget to take into account the absolute difference. englishThe absolute difference will flip the probability of all values below zero over the vertical axis and add them there. This will result in an increased slope to the right and a probability density of 0 for negative values .
Testing this empirically gives the following values: , , , , and . Which is consistent to the above formula.
we can calculate based on the previous probability density function (8) as
Testing this empirically gives the following values: , , , , and . Which is close to the results as given by eq. 11.
To determine we will calculate the probability distribution function of , which might appear as taking the long road when the probability distribution of uniform products on is already known . However, we will later on needs this probability distribution on to determine and and we can now introduce the technique we will also use in section 3.5.2.
The probability density function of is defined as the differential of the cumulative probability function, which in turn is defined as
Figure 2 illustrates how to find the number of values (of ) located below a fixed . The surface area that is not cut off at directly reflects the probability that a value below can be chosen because both and are uniformly distributed. Since the isoline of is located at , various forms of can be combined. suffers from a discontinuity at 0 when and its calculation in this particular context is further complicated with a sudden quadrant-change of the isoline when becomes negative. In which case quadrant 1 and 3 no longer contain the isoline. Instead, quadrant 2 and 4 will contain it (see figure 3 for an illustration). To approach this problem, we divided the calculation of CDF into three branches. The first with ; The second with and the last with .
Aside from these standard complications, we must also limit our and values to the range . This restricts the maximal surface area we can find and requires a piecewice approach. We start out with a general solutions for rectangles originating in and ending at a specific point. We have the following 4 sections:
Every section forms the two dimensional bounds (on and ) for the integral , and depending on the sign of they wil be added or substracted to/from each other. Figure 4 presents this graphically. When is positive we need to subtract and , otherwise we need to add them. is used to denoted the size of area intersected with the area below the isoline. The cumulative distribution function is now defined as
can be determined by first obtaining the x-point where the hyperbole crosses the top line of our box, which happens at position . When and are positive, this point will always be larger than 0. When , the intersection will happen outside , in which case the surface area will be . When we need to integrate and (See figure 5 for an illustration), which gives
The cumulative probability function (13) requires us to calculate the different sections based on (14) with ; and . Since (14) appears to be discontinue at position we split the cumulative probability function in 4 segments.
When the cumulative probability is 0 because there can be no values and , both larger than that can lead to a multiple that is smaller than .
(15), (16) and (17) are the non-normalized cumulative distribution functions americanwhen . Given the maximum surface area (17), we can divide and differentiate them all to provide us with the proper probability distribution (illustrated in figure 6). We conclude that, when :
When the result is somewhat more complex. Instead of subtracting , we need to add them. The interesting thing is that neither nor will intersect with , when . However, as soon as the situation is different. (depicted in fig. 3). Therefore we further branch the calculation.
When we are interested in the outside of the function , which is to be found in and . Since the area below the curve matches that as if , and were positive, we get surface area (The minus signs have been added to make and positive). As such, the remaining surface area must then be
The estimate of is calculated as
The situation when is discussed below. We first start with the case where . This gives uswe have the same result.
If we define and to be the translation and scaling of and as follows
This substitution will make it possible to rewrite
Without the condition that we need to double the estimate of (36)
The factors and are not independent, therefore we cannot rely on the standard rule for uniform products . In order to resolve this we divide the space of possible values in those which and those with . This gives, due to linearity of the following:
Plotting (figure 7) reveals the areas that we are interested in. The plots have been clipped at a specific value of . All positions where was larger than have been removed. So, the surface area of the plots that is not clipped forms the cumulative probability that a specific combination of will be lower than . Of course, we are not only interested in understanding the size of the area, but we also need to take into account the extra condition that .
For a fixed value of we can calculate the isoline as (taking into account that ) min . Figure 8 presents the shape of this function. The surface area under this curve is the area where while still respecting that . Integrating this equation provides us with the cumulative distribution function (39). To do so, we need to know where the linear 45 degree line cuts the function .
This now allows us to write down the surface area below as a couple of integrals
This function has a maximum of 0.5 at 1, so we need to multiply it by two to get a proper cumulative distribution function. Differentiating it afterward gives the probability density function of (left as calculated; right by sapping variables and ). Figure 9a plots (41).
Since we cannot rely on a symmetry around the 45 degree angle, we also need to calculate the surface area when . The isoline is given as minmax . Figure 8 provides more insight in the shape of this function. The function appears piecewise, with first a constant line (1) and then a hyperbolic piece up to its crossing with the 45 degree slope. Under these two segments we also have a linear piece that we need to subtract from the volume. For a fixed we determine the crossing of 1 with and the crossing between and . The first crossing occurs at position . The second crossing occurs at (40).
Knowing the value for makes it possible to finish (37) :
Testing this empirically gives the following values: , , , , , and . Which is correct with the above formula.
The correlation coefficient between and (7) is given as:
Figure 11 illustrates the correlation between and with . Since every block comparison is the summation of variables of the same distribution we get in the end a Gaussian distribution with means equal to the means of the underlying operators and a standard deviation that should be divided by . The standard deviations of and are
Which means that following the central limit theorem , we get, when , the following standard deviations for the block operators and .
(54) can be deduced as follows:
Since the second term is known to be zero (53), we have
we have that and thus (58) becomes
with this becomes
Both are interesting because they allow us to determine a confidence interval for the estimation based on the block size.
For a length we get a standard deviation of the prediction error of the -mismatch as . If we would work on the interval , we would get a prediction mismatch of . On the interval , the prediction mismatch would be . This seems large but the expected results for are large as well if we work with . For length , we get a standard deviation of . For a length of we have a standard deviation of . The standard deviation of the prediction based on for length is . For this becomes and gives .
It is easy to recognize that a baseline added to any of the two comparator blocks, will not influence the correlation value, it will merely increase the variance of the inproduct, while it will increase the value of the SAD. This results in predictions that might be 'off by a baseline'. Therefore, when using this technique in a sliding window setup, one might consider appropriate techniques to remove baselines, such as low pass filtering, multirate signal processing and so on [12, 17].
A second observation of importance is that zeros, in either block, will influence the reliability of the correlation. The more zeros, the more the SAD and the inproduct will differ. The inproduct essentially skips zeros and always returns a 'minimal match', irrelevant of the other value. The absolute difference will not be influenced by zeros, and will treat them as any other value, and thus still distinguish properly.
As a practical application we investigated the similarity between auto-correlation and the lagging sum of absolute differences, respectively defined as and :
The input dataset were the first samples of a song, which were then equalized and scaled to the range . On this dataset we calculated and , plotted in figure 12 in red and green respectively. The predicted values of and in function of the other are respectively purple and blue. We find that the predictions are fairly accurate and allow the recognition of the same peaks as one goes along. Near the end of the dataset appears biased due to the window size, which becomes smaller as the lag increases. reflects this in its variance. The similarity between and has already led to an application in meta data extraction, in which an algorithmic approach, using the SAD, assesses music-tempo slightly faster and with less memory requirements than conventional auto-correlation. This has been implemented in the DJ-program BpmDj [22, 23].
A second test we conducted was finding a pattern (called the needle) in a larger data set (the haystack). As a haystack we used the first equalized samples of a song. We generated two needles. The first one was a random white noise pattern, the second one was a random block taken from the haystack. Both needles were samples long. Using a sliding window block comparator we plotted the inproduct and the SAD, as well as their predicted values (Figure 13). As observed before, when the inproduct increases, the sad decreases and vice versa. Both plots start with an unexpected large sad and noisy inproduct because the song starts with some ambiance, which are many values near zero. Therefore in this area, the inproduct had a smaller variance than the rest of the song while the sad was larger than expected. The needle we looked for in the second test was taken from the middle of the haystack. At this position we indeed find a peaking of all values.
In auto-comparison and block searching we find the same peaks back and, skipping the baseline, at a local level the inproduct behaves similar to the SAD. This makes clear that whatever technique one uses to find a global match, both the inproduct and SAD are equally valid to find local minima. This has an important application in landmark tracking, which can be done equally well with Fourier transformations as with the widely used SAD.
The equalization of amplitudes can be easily conducted for sound waves or color images. There one simply counts the number of occurrences of specific amplitudes, which is an operation. Afterwards , a cumulative distribution of this binned histogram can be made. The total cumulative value can then form the divisor for all the binned numbers.
If is the number of time the value occurred in the audio-stream, then we define as
The maximal value is found at . ( standing for maximum amplitude). The equalization of the histogram now consists of assigning a floating point value to each of the original inputs and using those values. The lookup table will be called
Translating the original signal through will make it uniform distributed as
This operation is . It has however some memory lookups that might be unattractive for long sequences. The next question is whether on real signals, how well the correlation holds. Can we for instance find the
If two data sequences are uniformly sampled from -1:1 then both the summed inproduct (SIP) and the sum of absolute differences (SAD) behave similarly, with a correlation coefficient of -0.8485. This useful relationship allows the prediction of one operator in function of the other as follows:
Applications of this relation might, among others, include optimizations in the areas of sound analysis, pattern-matching, digital image processing and compression algorithms (such as rsync).
The use of absolute difference was attractive because it offers a way to compare signals such that mismatches could not cancel each other out thereby providing the same result independent of any constant added to the signal. Convolution did not behave as such and in fact provided, in our view, not the best manner of comparing two signals: it was dependent on the constant added to the signal (which can happen due to very low frequency oscillation that cannot be captured in the window). On the other hand, when many comparisons are necessary, the inproduct can be implemented very efficiently by means of Fourier transformation.
Regarding the proof and formulas used in this document: it is probably the worst piece of math I've ever been involved with. It is tedious, complicated and at the moment I have no clue how I could simplify it. Probably there are some black magic tricks mathematicians could use, but not being a native mathematician, I'm stuck with what I have.
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