Kadir brady saliency detector

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Kadir Brady saliency detector is one of the feature detectors which aims to detect representative , discriminative, hence, salient region in the image. It has certain useful property so that it often performs well in the context of object class recognition. Its similarity invariant version was first invented by Timor Kadir and Michael Brady in 2001 ^[1]. Later in 2004, the affine invariant version was invented by Kadir, T., Zisserman, A. and M. Brady. ^[2](A. Baumberg 2000).

1 Introduction
2 Information theoretic saliency
3 Performance evluation
- 3.1 Performance under global transformation
- 3.2 Performance under intra-class variation and image perturbations
  - 3.2.1 Intra-class variation test
  - 3.2.2 Image perturbations test
4 Conclusion
5 Software implementation
6 References
7 External links
8 See also

[edit] Introduction

As Feature detection mentioned, many computer vision and image processing applications work directly with the features extracted from image, rather than the raw image, for example, for computing image correspondences [2, 17, 19,20, 22], or for learning object categories [1, 3, 4, 23]. Depenands on the applications, different characteristics are preferred. However, there are three broad classes of image change under which good performance may be required:

Global transformation: Features should be repeatable across the expected class of global image transformations. These include both geometric and photometric transformations that arise due to changes in the imaging conditions. For example, region detection should be covariant with viewpoint as illustrated in Figure 1. In short, we require the segmentation to commute with viewpoint change. This property will be evaluated on the repeatability and accuracy of localization and region estimation.
Local perturbations: Features should be insensitive to classes of semi-local image disturbances. For example, a feature responding to the eye of a human face should be unaﬀected by any motion of the mouth. A second class of disturbance is where a region neighbours a foreground/background boundary. The detector can be required to detect the foreground region despite changes in the background.
Intra-class variations: Features should capture corresponding object parts under intra-class variations in objects. For example, the headlight of a car for different brands of car (imaged from the same viewpoint).

All Feature detection algorithms are trying to detect regions which is stable under three types of image change described above. In stead of finding corner[7 21], or blob[17 22], or any specific shape of regions, Kadir brady saliency detector looks for regions which are locally complex, and globally discriminative. Such regions usually corresponds to region more stable under these types of image change.

[edit] Information theoretic saliency

In the field of Information theory, Shannon entropy is defined to quantify the complexity of a distribution p as $p \log p \,$ . Therefore, higher entropy means p is more complex, hence more unpredictable.

To measure the complexity of an image region ${x, R}$ around point $x$ with shape $R$ , a descriptor $D$ that takes on values $d 1,..., d r$ (e.g. in an 8 bit grey level image, D would range from 0 to 255 for each pixel) is defined so that $P D (d i, x, R)$ , the probability of descriptor value $d i$ occurs in region ${x, R}$ can be computed. Further, the entropy of image region $R x$ can compute as

$H_{D}(x,R) = -\sum_{i \in (1...r)} P_{D}(d_i,x,R) \log P_{D}(d_i,x,R).$

Using this entropy equation we can further calculate $H D (x, R)$ for every point $x$ and region shape $R$ . As shown in Figure 2 (a), more complex region, like eye region, has more complex distribtion, hence higher entropy.

$H D (x, R)$ is a good measure for local complexity. However, entropy only measures the statistic of local attribute. It doesn't measure the spatial arrangement of the local attribute. For example, figure 2 (b) shows three permutations of pixels of the eye region which have the same entropy as the eye region. However, these four regions are not equally discriminative under scale change as shown in figure 2 (b). This observation is used to define measure on discriminative in subsections.

The following subsections will discuss different methods to select regions with high local complexity and more discriminative across different region.

[edit] Similarity invariant saliency

The first version of Kadir brady saliency detector[10] only finds Salient regions invariant under similarity transformation. The algorithm finds circle regions with different scales. In another words, given $H D (x, s)$ , where s is the scale parameter of a circle region $R$ , the algorithm select a set of circle region, ${x i, s i; i = 1... N}$ .

The method consists of three steps:

Calculation of Shannon entropy of local image attributes for each x over a range of scales — $H_{D}(x,s) = -\sum_{i \in (1...r)} P_{D}(d_i,x,s) \log P_{D}(d_i,x,s)$ ;
Select scales at which the entropy over scale function exhibits a peak — $s p$ ;
Calculate the magnitude change of the PDF as a function of scale at each peak — $W_D(x,s) = \sum_{i \in (1...r)} |\frac{\part}{\part s}P_{D,}(d_i,x,s)|$ (s).

The ﬁnal saliency $Y D (x, s p)$ is the product of $H D (x, s p)$ and $W D (x, s p)$ .

For each x the method picks a scale $s p$ and calculates salient score $Y D (x, s p)$ . By comparing $Y D (x, s p)$ of different points $x$ the detector can rank the saliency of points and pick the most representative ones. For example, in Figure 3, blue circle region has higher saliency than red circle.

[edit] Affine invariant saliency

Previous method is invariant to the similarity group of geometric transformations and to photometric shifts. However, as mentioned in the opening remarks, the ideal detector should detect region invariant up to viewpoint change. There are several detector [] can detect affine invariant region which is a better approximation of viewpoint change than similarity transformation.

To detect affine invariant region, the detector need to detect ellipse as in figure 4. $R$ now is parameterized by three parameter (s, "ρ", "θ"), where "ρ" is the axis ratio and "θ" the orientation of the ellipse.

This modification increases the search space of the previous algorithm from a scale to a set of parameters. Therefore the complexity of the affine invariant saliency detector increases. In practice the affine invariant saliency detector starts with the set of points and scales generate from the Similarity invariant saliency detector, then iteratively approximates the suboptimal parameters.

[edit] Comparison

Although similarity invariant saliency detector is faster than Affine invariant saliency detector, it also has the drawback of favoring isotropic structure since the discriminative measure $W D$ is measured over isotropic scale. To summarize, Affine invariant saliency detector is invariant to affine transformation and able to detect more generate salient regions.

Figure 5 and 6 shows the comparison image output from both Similarity invariant saliency detector and invariant saliency detector.

[edit] Salient Volumn

It is intuitive to pick points from higher salient score directly and stop when a certain number of threshold on number of points or salient score is satisfied. However, natural images contain noise and motion blur, both of them act as randomisers and generally increase entropy, affecting previously low entropy values more than high entropy values.

A more robust method would be to pick regions rather than points in entropy space. Although the individual pixels within a salient region may be affected at any given instant by the noise, it is unlikely to affect all of them in such a way that the region as a whole becomes non-salient.

It is also necessary to analyze the whole saliency space such that each salient feature is represented. A global threshold approach would result in highly salient features in one part of the image dominating the rest. A local threshold approach would require the setting of another scale parameter.

A simple clustering algorithm meets these two requirements are used at the end of the algorithm. It works by selecting highly salient points that have local support - that is, nearby points with similar saliency and scale. Each region must be sufficiently distant from all others (in R3 ) to qualify as a separate entity. For robustness, we use a representation that includes all of the points in a selected region. The method works as follows:

Apply a global threshold.
Choose the highest salient point in saliency-space (Y).
Find the K nearest neighbours (K is a pre-set constant).
Test the support of these using variance of the centre points.
Find distance, D, in R3 from salient regions already clustered.
Accept, if D > scalemean of the region and if suﬃciently clustered (variance is less than pre-set threshold Vth ).
Store as the mean scale and spatial location of K points.
Repeat from step 2 with next highest salient point.

The algorithm is implement as GreedyCluster1.m in matlab by Dr. Timor Kadir which can be download here

[edit] Performance evluation

In the field ofcomputer vision, different feature detectors have been evaluated by several tests. The most profound evaluation is published on International Journal of Computer Vision in 2006 ^[3]. The following subsection discuss the performance of Kadir brady saliency detector on a subset of test in the paper.

[edit] Performance under global transformation

In order to measure the consistency of region detected on the same object or scene across images under global transformation, repeatability score, which is first proposed by Mikolajczyk and Cordelia Schmid in [18, 19], is calculated as follow.

Firstly, overlap error $ε$ of a pair of corresponding ellipses $μ a$ and $μ b$ each on different images is defined

$\epsilon = 1 - \frac{\mu_a \cap (A^T \mu_b A)}{\mu_a \cup (A^T \mu_b A)}$

where A is the locally linearized affine transformation of the homography between the two images, and $\mu_a \cap (A^T \mu_b A)$ and $\mu_a \cup (A^T \mu_b A)$ represent the area of intersection and union of the ellipses respectively. Notice $μ a$ is scaled into a fix scale to take the count of size variation of different detected region. Only if $ε$ is smaller than certain $ε 0$ , the pair of ellipses are deemed to correspond.

Then the repeatability score for a given pair of images is computed as the ratio between the number of region-to-region correspondences and the smaller of the number of regions in the pair of images, where only the regions located in the part of the scene present in both images are counted. In general we would like a detector to have a high repeatability score and a large number of correspondences.

The specific global transformations tested in the [test dataset] are:

Viewpoint change
Zoom+rotation
Image blur
JPEG compression
Light change

The performance of Kadir Brady saliency detector is inferior to most of other detectors mainly because the number of points detected is usually lower than other detectors.

The precise procedure is given in the Matlab code from Detector evaluation #Software implementation.

[edit] Performance under intra-class variation and image perturbations

In the task of object class categorization, the ability of detecting similar regions given intra-class variation and image perturbations across object instance is very critical. In [cite], Repeatability measure over intra-class variation and image perturbations is proposed. The following subsection will introduce the definition and discuss the performance.

[edit] Intra-class variation test

Suppose there are a set of images of the same object class, e.g. motorbikes. A region detection operator which is unaffected by intra-class variation will reliably select regions on corresponding parts of all the objects, say the wheels, engine or seat for motorbikes.

Repeatability over intra-class variation is measuring the (average) number of correct correspondences over the set of images, where the correct correspondences is established by manual selection.

A region is matched if it fulﬁls three requirements:

Its position matches within 10 pixels.
Its scale is within 20%.
Normalised mutual information between the appearances is > 0.4.

In detail the average correspondence score S is measured as follows.

N regions are detected on each image of the M images in the dataset. Then for a particular reference image i the correspondence score $S i$ is given by the proportion of corresponding to detected regions for all the other images in the dataset, i.e.:

$Si = \frac{Total\ number\ of\ matches}{Total\ number\ of\ detected\ regions}=\frac{N_{M}^{i}}{N (M-1)}$

The score $S i$ is computed for M/2 different selections of the reference image, and averaged to give S. The score is evaluated as a function of the number of detected regions N .

Kadir brady saliency detector gives highest score across three test class, which are motorbike, car, and face. As illustrate in figure [], saliency detector indicates that most detections are near the object. In contrast, other detectors maps show a much more diffuse pattern over the entire area caused by poor localisation and false responses to background clutter.

[edit] Image perturbations test

In order to test insensitivity to image perturbation the data set is split into two parts: the ﬁrst contains images with a uniform background and the second, images with varying degrees of background clutter. If the detector is robust to background clutter then the average correspondence score S should be similar for both subsets of images.

In this test saliency detector also outperforms other detectors duo to three reasons:

Several detection methods blur the image, hence causing a greater degree of similarity between

objects and background.

In most images the objects of interest tend to be in focus while backgrounds are out of focus and hence blurred. Blurred regions tend to exhibit slowly varying statistics which result in a relatively low entropy and inter-scale saliency in the saliency detector.
Other detectors deﬁne saliency with respect to specific properties of the local surface geometry. In contrast, the saliency detector uses a much broader definition.

[edit] Conclusion

Saliency detector is most useful in the task of object recognition, whereas several other detector are more useful in the task of computing image correspondences. However, in the task of 3D object recognition which all three type of image change are combined, Saliency detector might still be powerful as mentioned in [xx].

[edit] Software implementation

[edit] References

^ Scale, Saliency and Image Description. Timor Kadir and Michael Brady. International Journal of Computer Vision. 45 (2):83-105, November 2001
^ Kadir, T., Zisserman, A. and Brady, M. An affine invariant salient region detector. Proceedings of the 8th European Conference on Computer Vision, Prague, Czech Republic (2004)
^ A comparison of affine region detectors. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir and L. Van Gool. International Journal of Computer Vision

A. Baumberg (2000). "Reliable feature matching across widely separated views". Proceedings of IEEE Conference on Computer Vision and Pattern Recognition: pages I:1774--1781.
J. Matas, O. Chum, M. Urban, and T. Pajdla (2002). "Robust wide baseline stereo from maximally stable extremal regions". Proceedings of British Machine Vision Conference: pages 384–393.
K. Mikolajczyk and C. Schmid (2002). "An aﬃne invariant interest point detector". Proceedings of European Conference on Computer Vision.
F. Schaﬀalitzky and A. Zisserman (2002). "Multi-view matching for unordered image sets, or “How do I organize my holiday snaps?”". Proceedings of European Conference on Computer Vision: 414–431.
T. Tuytelaars and L. Van Gool (2000). "Wide baseline stereo based on local, aﬃnely invariant regions". Proceedings of British Machine Vision Conference: 412–422.
S. Agarwal and D. Roth (2002). "Learning a sparse representation for object detection". Proceedings of European Conference on Computer Vision: 113–130.
E. Borenstein and S. Ullman (2002). "Class-speciﬁc, top-down segmentation". Proceedings of European Conference on Computer Vision: 109–124.
R. Fergus, P. Perona, and A. Zisserman (2003). "Object class recognition by unsupervised scale-invariant learning". Proceedings of IEEE Conference on Computer Vision and Pattern Recognition: II:264–271.
M. Weber, M. Welling, and P. Perona (June 20002). "Unsupervised learning of models for recognition". Proceedings of European Conference on Computer Vision.

[edit] External links

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