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This page presents a collection of results obtained with the proposed
top-down discriminant
saliency detector (DSD). In all experiments, the detector is trained
and tested with unsegmented images containing cluttered backgrounds.
Saliency examples from
PASCAL 2006 database
The figure below shows some example saliency maps produced by DSD for
various images (and objects classes) from the PASCAL 2006 database.
All scenes contain instances from two object classes, e.g. ``person'' and
``car'', or ``motorbike'' and ''car''. In each case, a bounding box is drawn
around the object of interest. The saliency map for the detection of that
object is shown on the right. It is clear that DSD successfully switches
between the two objects, highlighting the one of interest and suppressing
all others. This ability is a significant advantage of top-down saliency over
bottom-up interest point detection.
Comparison of object localization accuracy on
PASCAL 2006 database
In this experiment, the DSD is used to implement a focus-of-attention
mechanism that prunes bottom-up (BU) interest points which are not important
for the detection of the objects of interest. The object localization accuracy of
the selected points (DSD-BU) is measured with precision-recall (PR) curves.
The performance of DSD-BU is compared to those of other top-down pruning
strategies:
| Discriminant Visual Words (DVW) |
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clusters of BU points, measuring discriminability by posterior probability of
interest points [Bouveyron, et al., ICVGIP 2006; Chum & Zisserman, CVPR 2007].
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| linear SVM (LSVM) |
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clusters of BU points, measuring discriminability
by the weight assigned to visual words by a linear SVM classifier
[Jurie & Triggs ICCV 2005]. |
| probabilistic Latent Semantic Analysis
(pLSA) |
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clusters of BU points, based on probabilistic topic discovery for each
object class [Sivic et al. ICCV 2005]. |
In all cases, discriminant saliency achieves substantially better performance.
Comparison of salient locations detected on
PASCAL 2006
The figure below shows some examples of the BU interest points selected
(at 40% recall rate) by DSD and other methods. From top to bottom:
original images (objects marked by their bounding boxes), interest points
selected by DSD, DVW, LSVM, and pLSA. Each circle in the image represents
the location and size of a salient point. The white color indicates the points
which fall inside the segmentation ground truth (the bounding box marked
on the original image), while black indicates the opposite.
More detailed DSD results, for each of the object classes in PASCAL 2006,
are available here:
bicycle,
bus,
car,
cat,
dog,
horse,
motorbike,
person.
Examples from the Brodatz
texture database
The figure below shows examples of saliency maps for various textures.
These examples show that discriminant saliency can
1) ignore highly textured backgrounds in favor of more salient foreground objects,
and 2) detect as salient a wide variety of shapes, and contours of different
crispness and scale, or even texture gradients.
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