Distributed High Dimensional Information Theoretical Image Registration via Random Projections✩

Distributed High Dimensional Information Theoretical Image Registration via Random Projections

Zoltán Szabó    András Lőrincz
Abstract

Information theoretical measures, such as entropy, mutual information, and various divergences, exhibit robust characteristics in image registration applications. However, the estimation of these quantities is computationally intensive in high dimensions. On the other hand, consistent estimation from pairwise distances of the sample points is possible, which suits random projection (RP) based low dimensional embeddings. We adapt the RP technique to this task by means of a simple ensemble method. To the best of our knowledge, this is the first distributed, RP based information theoretical image registration approach. The efficiency of the method is demonstrated through numerical examples.

Distributed High Dimensional Information Theoretical Image Registration via Random Projections

Eötvös Loránd University, Department of Software Technology and Methodology Pázmány Péter sétány 1/C, Budapest, H-1117, Hungary


 

Keywords: random projection, information theoretical image registration, high dimensional features, distributed solution

 

footnotetext: © 2012 Elsevier Inc. Digital Signal Processing 22(6):894-902, 2012. The original publication is available at http://dx.doi.org/10.1016/j.dsp.2012.04.018.$\ast$$\ast$footnotetext: Corresponding author. Fax: +36 1 381 2140.

1 Introduction

Machine learning methods are notoriously limited by the high dimensional nature of the data. This problem may be alleviated via the random projection (RP) technique, which has been successfully applied, e.g., in the fields of classification Fradkin and Madigan (2003); Deegalla and Boström (2006); Goel et al. (????), clustering Fern and Brodley (2003), independent subspace analysis Szabó and Lőrincz (2009), search for approximate nearest neighbors Ailon and Chazelle (????), dimension estimation of manifolds Hegde et al. (????), estimation of geodesic paths Mahmoudi et al. (2008), learning mixture of Gaussian models Dasgupta (2000), compression of image and text data Bingham and Mannila (2001), data stream computation Li et al. (2007); Menon et al. (2007) and reservoir computing Lukos̆evic̆ius and Jaeger (2009). For a recent RP review, see Vempala (2005). We note that the RP technique is closely related to the signal processing method of compressed sensing Baraniuk et al. (2008).

As it has been shown recently in a number of works Özuysal et al. (2010); Kokiopoulou et al. (2009); Akselrod-Ballin et al. (2009); Healy and Rohde (2007), the RP approach has potentials in patch classification and image registration. For example, Özuysal et al. (2010) combines the votes of random binary feature groups (ferns) for the classification of random patches in a naive Bayes framework. Promising registration methods using and (Euclidean distance, correlation) norms have been introduced in Kokiopoulou et al. (2009) and Akselrod-Ballin et al. (2009); Healy and Rohde (2007), respectively.

Information theoretical cost functions, however, exhibit more robust properties in multi-modal image registration Neemuchwala et al. (2007); Kybic (2004); Bardera et al. (2006). Papers Neemuchwala et al. (2007); Kybic (2004) apply k-nearest neighbor based estimation. However, the computation of these quantities is costly in high dimensions Arya et al. (1998) and the different image properties (e.g., colors, intensities of neighborhood pixels, gradient information, output of spatial filters, texture descriptors) may easily lead to high dimensional representation. The task is formulated as the estimation of discrete mutual information in Bardera et al. (2006) and the solution is accomplished by equidistant sampling of points from randomly positioned straight lines. The method estimates a histogram of bins, where is the number of bins of the image, which may considerably limit computational efficiency.

Here we address the problem of information theoretical image registration in case of high dimensional features. Particularly, we demonstrate that Shannon’s multidimensional differential entropy can be efficiently estimated for high dimensional image registration purposes through RP methods. Our solution enables distributed evaluation. The presented approach extends the method presented in Szabó and Lőrincz (2009) in the context of independent subspace analysis (ISA) Cardoso (1998), where we exploited the fact that ISA can be formulated as the optimization problem of the sum of entropies under certain conditions Szabó et al. (2007). Here, to our best knowledge, we present the first distributed RP based information theoretical image registration approach.

The paper is structured as follows: In Section 2 we shortly review the image registration problem as well as the method of random projections. Section 3 formulates our RP based solution idea for image registration. Section 4 contains the numerical illustrations. Conclusions are drawn in Section 5.

2 Background

First, we describe the image registration task (Section 2.1) followed by low distortion embeddings and random projections (Section 2.2).

2.1 The Image Registration Problem

In image registration one has two images, and , as well as a family of geometrical transformations, such as scaling, translation, affine transformations, and warping. We assume that the transformations can be described by some parameter and let denote the set of the possible parameters. Let transformation with parameter on produce . The goal of image registration is to find the transformation (parameter ) for which the warped test image is the ‘closest’ possible to reference image . Formally, the task is

where the similarity of two images is given by the similarity measure . Registration depends on the similarity measure and one use – among other things – and norm, or different information theoretical similarity measures.

Let feature denote the feature of image associated with pixel . In the simplest case, the feature is the pixel itself, but one can choose a neighborhood of the pixel, edge information at and around the pixel, the RGB values for colored images, or combinations of these. For registrations based on the norm , the cost function takes the form

where for a vector . Instead of the similarity of features in and norms, one might consider similarity by means of information theoretical concepts. An example is that we take the negative value of the joint entropy of the features of images , as our cost function García-Arteaga and Kybic (2008):

(1)

where denotes Shannon’s multidimensional differential entropy Cover and Thomas (1991). One may replace entropy in (1) by other quantities, e.g., by the Rényi’s -entropy, the -mutual information, and the -divergence, to mention some of the candidate similarity measures Neemuchwala et al. (2007).

2.2 Low Distortion Embeddings, Random Projection

Low distortion embedding and random projections are relevant for our purposes. Low distortion embedding intends to map a set of points of a high dimensional Euclidean space to a much lower dimensional one by preserving the distances between the points approximately. Such low dimensional approximate isometric embedding exists according to the Johnson-Lindenstrauss Lemma Johnson and Lindenstrauss (1984):

Lemma (Johnson-Lindenstrauss).

Given a number and a point set of elements. Then for there exists a Lipschitz mapping such that

(2)

for any .

During the years, a number of explicit constructions have appeared for the construction of . Notably, one can show that the property embraced by (2) is satisfied with probability that approaches 1 for random linear mapping (, ) provided that is chosen to project to a random -dimensional subspace Frankl and Maehara (1987).

Less strict conditions on are also sufficient and many of them decreases computational costs. Introducing the notation , i.e., , , it is sufficient that matrix elements of matrix are drawn independently from the standard normal distribution Indyk and Motwani (????).111Multiplier in expression means that the length of the rows of matrix is not strictly one; it is sufficient if their lengths are 1 on the average. Other explicit constructions for include Rademacher and (very) sparse distributions Achlioptas (2003); Li et al. (2006). More general methods are also available based on weak moment constraints Arriga and Vempala (2006); Matous̆ek (2008).

3 Method

In image registration information theoretical registration measures show robust characteristics when compared with and measures222The family of measures include the correlation defined by the scalar product., e.g., in Kybic (2004) directly for (1), and for -entropy, -mutual information, and -divergence in Neemuchwala et al. (2007). However, these estimations have high computational burdens since the dimension of the features in the cited references are and respectively. Here, we deal with the efficient estimation of cost function (1) that from now on we denote by . We note that the idea of efficient estimation can be used for a number of information theoretical quantities, provided that they can be estimated by means of pairwise Euclidean distances of the samples.

Central to our RP based distributed method are the following:

  1. The computational load can be decreased by

    1. dividing the samples into groups and then

    2. computing the averages of the group estimates Kybic (2004).

    We call this the ensemble approach.

  2. Estimation of the multidimensional entropy cost function can be carried out consistently by nearest neighbor methods using pairwise Euclidean distances of sample points Kozachenko and Leonenko (1987); Hero et al. (2002); Leonenko et al. (2008).

Taking into account that low dimensional approximate isometric embedding of points of high dimensional Euclidean space can be addressed by the Johnson-Lindenstrauss Lemma and the related random projection methods, we suggest the following procedure for distributed RP based entropy (and thus ) estimation:

  1. divide the feature samples333In the image registration task the set of feature samples is where is the running index and denotes the concatenation of vectors and . into groups indexed by sets so that each group contains samples,

  2. for all fixed groups take the random projection of as

    Note: normalization factor can be dropped in since it becomes an additive constant term for the case of the differential entropy, .

  3. average the estimated entropies of the RP-ed groups to get the estimation

    (3)

In the next section we illustrate the efficiency of the proposed RP based approach in image registration.

4 Illustrations

In our illustrations, we show examples that enable quantitative evaluation and reproduction:

  1. We use images:

    1. In test Lena, we register the rotated versions of the red and green channels of image Lena, see Fig. 1(a).

    2. In the mandrill test we register the rotated versions of the gray-scale image of a mandrill baboon and its Sobel filtered version, see Fig. 1(b).

  2. we chose to evaluate the objective function (1) for angles from to by steps and in interval by steps . In the ideal case the optimal degree is . Our performance measure is the deviation from the optimal value.

In our simulations,

  • we chose the rectangle around each pixel as the feature of that pixel.

  • coordinates of the RP matrices were drawn independently from standard normal distribution, but more general constructions could also be used Arriga and Vempala (2006); Matous̆ek (2008).

  • for each individual parameter, random runs were averaged. Our parameters included , the linear size of the neighborhood that determines dimension of the feature, , the size of the randomly projected groups and , the dimension of RP.

  • performance statistics are summarized by means of notched boxed plots, which show the quartiles (), depict the outliers, i.e., those that fall outside of interval by circles, and whiskers represent the largest and smallest non-outlier data points.

  • we studied the efficiency of five different entropy estimating methods in (3) including

    • the recursive k-d partitioning scheme Stowell and Plumbley (2009),

    • the -nearest neighbor method Leonenko et al. (2008),

    • generalized k-nearest neighbor graphs Pál et al. (????),

    • minimum spanning tree based entropy graphs Yukich (1998); Hero et al. (2002) and

    • the weighted nearest neighbor method Sricharan and Hero (????).

    The methods will be referred to as kdp, kNN, kNN, MST and wkNN, respectively. kdp is a plug-in type method estimating the underlying density directly, hence especially efficient for small dimensional () problems. Pál et al. (????) extends the approach of Leonenko et al. (2008) () to an arbitrary neighborhood subset (). In our experiments, we set . Instead of k-nearest graphs the total sum of pairwise distances is minimized over spanning trees in the MST method. The kNN, kNN, MST constructions belong to the general umbrella of quasi-additive functionals Yukich (1998) providing statistically consistent estimation for the Rényi entropy () Rényi (????) . The Shannon’s entropy is a special case of this family since . In our simulations, we chose . , the number of neighbors in kNN, kNN was . Finally, the wkNN technique makes use of a weighted combination of k-nearest neighbor estimators for different values.

  • , the neighborhood parameter was selected from the set .

  • , the size of groups and , the RP dimension took values , , , , , and , respectively.

  • the feature points were distributed randomly into groups of size in order to increase the diversity of the individual groups.

In the first set of experiments we focused on the precision of the estimations on the Lena dataset. According to our experiences

  • there is no relevant/visible difference in the precision of the estimations for . The estimation is even of high precision for that we illustrate in Fig. 2(a)-(b) for the kdp technique. The estimation errors are quite similar for and , the latter is shown in Fig. 2(c)-(d). Here, one can notice a small uncertainty in the estimations for smaller RP dimensions (), which is moderately present for larger values () – except for the largest studied group size .

  • the obtained results are of similar precision on this dataset for all the studied entropy estimators. We illustrate this property for the most challenging value in Fig. 3 and Fig. 4.

In the second set of experiments we were dealing with the mandrill dataset, where different modalities of the same image (pixel, edge filtered version) had to be registered. Here,

  • the kdp approach gradually deteriorates as the dimension of the underlying feature representation is increasing, i.e., as a function of . For , the method gives precise estimations for and small group sizes (); other parameter choices result in uncertain estimations, see Fig. 5(a)-(b). By increasing the size of neighborhood (), the estimations gradually break down. For , the precisions are depicted in Fig. 5(c)-(d); the estimations are still acceptable. For we did not obtain valuable estimations for the kdp technique.

  • in contrast to the kdp method, the kNN, kNN, MST and wkNN techniques are all capable of coping with the and values, as it is illustrated in Fig. 6, Fig. 7, Fig. 8 and Fig. 9, respectively. It can also be observed, that the RP dimension must be here, and in case of one obtaines highly precise/certain estimations.

  • the only method which could cope with the increased neighbor size value, was the technique. This result could be achieved for RP dimension making use of small group sizes (), see Fig. 10.

The computation times are illustrated for the Lena () and mandrill dataset () for the kdp method in Fig.11(a) and Fig.11(b), respectively. As it can be seen, the ensemble approach with group size may speed up computations by several orders of magnitudes; similar trends can be obtained for the other estimators, too. Among the studied methods, the kdp technique was the most competitive in terms of computation time. We also present the computation times for the largest studied problem, Lena with ; compared to kdp

  • the kNN and kNN techniques were within a factor of in terms of computation time,

  • the wkNN method was () times slower compared to the kdp approach in case of (), and

  • the MST based estimator was within a factor of compared to kdp in case of , and more than times slower for .

As it can be seen in Fig. 11, the application of the reduced RP dimension can be advantageous in terms of computation time. Moreover, compared to schemes without dimensionality reduction ( and , ), i.e., working directly on raw data, the presented RP based dimensionality approach can heavily speed-up computations. This behaviour is already present for , as it is illustrated for on the mandrill dataset in Table 1.

Considering the possible choices, according to our numerical experiences,

  • often, small RP dimensions give rise to reliable estimations for several entropy methods,

  • it is necessary to slowly increase as a function of the dimension of the feature representation (parameterized by ),

  • in the studied parameter domain, group sizes of could provide precise estimations, and simultaneously open the door to massive speed-up by distributed solutions.

These results demonstrate the efficiency of our RP based approach.

5 Conclusions

We have shown that the random projection (RP) technique can be adapted to distributed information theoretical image registration. Our extensive numerical experiments including five different entropy estimators demonstrated that the proposed approach (i) can offer orders of magnitude in computation time, and (ii) provides robust estimation for large dimensional features.

It is very promising since it is parallel and fits multi-core architectures, including graphical processors. Since information theoretical measures are robust, our method may be useful in diverse signal processing areas with the advance of multi-core hardware.

Acknowledgments

The European Union and the European Social Fund have provided financial support to the project under the grant agreement no. TÁMOP 4.2.1./B-09/1/KMR-2010-0003. The research has also been supported by the ‘European Robotic Surgery’ EC FP7 grant (no.: 288233). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of other members of the consortium or the European Commission.

The authors would like to thank to Kumar Sricharan for making available the implementation of the wkNN method.

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About the author–ZOLTÁN SZABÓ (Applied Mathematics M.Sc. 2006, Ph.D. 2012, Informatics Ph.D. 2009) is a research fellow at the Eötvös Loránd University. In 2007, he won the Scientist of the Year Award of the Faculty of Informatics. In 2008, he obtained the Bronze Medal of the Pro Patria et Scientia Award of Hungarian Ph.D. Students. He is a reviewer at the IEEE Transactions on Neural Networks and Learning Systems, Signal, Image and Video Processing, Neurocomputing and IEEE Transactions on Signal Processing journals. His research interest include Independent Subspace Analysis and its extensions, information theory, kernel methods, group-structured dictionary learning and collaborative filtering.

About the author–ANDRÁS LŐRINCZ (Physics M.Sc. 1975, Solid State Physics Ph.D. 1978, Molecular Physics C.Sc. 1986, Laser Physics habilitation, 1998, Information Technology habilitation, 2009) is a senior researcher of Information Science at Eötvös Loránd University. He is a Fellow of the European Coordinating Committee for Artificial Intelligence. He has published more than 140 peer reviewed journal 80 peer reviewed conference papers on his research areas. He has been leading a group working on different aspects of intelligent systems.

(a)
(b)
Figure 1: Illustration of the (a): Lena test, (b): mandrill test.
(a)
(b)
(c)
(d)
Figure 2: Estimation error as a function of the RP dimension on the Lena dataset for different group sizes. Method: kdp. (a)-(b): . (c)-(d): . First column: , . Second column: , .
(a)
(b)
(c)
(d)
Figure 3: Estimation error as a function of the RP dimension on the Lena dataset for different group sizes. Neighbor size: . (a)-(b): kNN method. (c)-(d): kNN method. First column: , . Second column: , .
(a)
(b)
(c)
(d)
Figure 4: Estimation error as a function of the RP dimension on the Lena dataset for different group sizes. Neighbor size: . (a)-(b): MST method. (c)-(d): wkNN method. First column: , . Second column: , .
(a)
(b)
(c)
(d)
Figure 5: Estimation error as a function of the RP dimension on the mandrill dataset for different group sizes. Method: kdp. (a)-(b): neighbor size . (c)-(d): . First column: , . Second column: , .
(a)
(b)
(c)
(d)
Figure 6: Estimation error as a function of the RP dimension on the mandrill dataset for different group sizes. Method: kNN. (a)-(b): neighbor size . (c)-(d): . First column: , . Second column: , .
(a)
(b)
(c)
(d)
Figure 7: Estimation error as a function of the RP dimension on the mandrill dataset for different group sizes. Method: kNN. (a)-(b): neighbor size . (c)-(d): . First column: , . Second column: , .
(a)
(b)
(c)
(d)
Figure 8: Estimation error as a function of the RP dimension on the mandrill dataset for different group sizes. Method: MST. (a)-(b): neighbor size . (c)-(d): . First column: , . Second column: , .
(a)
(b)
(c)
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Figure 9: Estimation error as a function of the RP dimension on the mandrill dataset for different group sizes. Method: wkNN. (a)-(b): neighbor size . (c)-(d): . First column: , . Second column: , .
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Figure 10: Estimation error for RP dimension and for different group sizes. Method: wkNN. Neighbor size: .
(a)
(b)
Figure 11: Computation time as a function of the RP dimension for different group sizes with log scale on the axis. Method: kdp. (a): Lena dataset, neighbor size . (b): mandrill dataset, .
kdp
kNN (kNN)
MST
wkNN
Table 1: Computation time versus raw data based method. Value , means times improvement in computation time over the method not applying dimension reduction. Dataset: mandrill. Baseline: RP dimension . Neighbor size: .
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