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NEC Research Institute, Princeton, NJ, USA.
[CMM^{+}00].
The current version of the systems uses color histogram and color spatial
distribution along with hidden textual annotations.
Besides a 64-bin HSV histogram, two other vectors - a 256-length HSV color
autocorrellogram (CORR) and a 128-length RGB color-coherence vector (CCV) -
are describing the color content of an image.
The first 64 components of CORR represent the number of pixels of a particular
color from the 64-quantized HSV color space having neighbors of the same color
at distance 1.
The rest of the vector is defined in the same way for distances 3, 5 and 7.
CCV is made of 64 coherence pairs, each pair giving the number of coherent,
and incoherent pixels of a particular discretized color in the RGB space.
In order to classify pixels in one of the two categories, the image is first
blurred slightly by replacing each pixel value with the average value of the
pixels in its
neighborhood.
Then, pixels are grouped in connected components.
A pixel will be classified as coherent if the connected component it belongs
to is larger than a fixed threshold.
Keywords, selected from a set of 138 words, are associated with each image in
the database population step and are represented as a boolean vector.
Retrieval is done with query by example.
The distance between individual features (color vectors or annotation lists represented as
binary vectors) is the *L*_{1} distance. These distances are scaled and combined in a global distance.
The scaling factors are computed by maximizing the probability of a training set.
PicHunter implements a probabilistic relevance feedback mechanism, which tries to predict
the target image the user wants based on his actions (the images he selects as similar
to the target in each iteration of a query session).
A vector is used for retaining each image's probability of being the target. This
vector is updated at each iteration of the relevance feedback, based on the history of the
session (images displayed by the system and user's actions in previous iterations).
The updating formula is based on Bayes' rule. If the *n* database images are noted
*T*_{j},
,
and the history of the session through iteration *t* is denoted
,
with *D*_{j} and *A*_{j} being the images displayed
by the system and, respectively, the action taken by the user at the iteration *j*, then
the iterative update of the probability estimate of an image *T*_{i} being the target, given
the history *H*_{t}, is:

In computing the probability of a user to take a certain action *A*_{t} given the history so far
and the fact that the target is indeed *T*_{i}, namely
*P*(*A*_{t}|*T*=*T*_{i},*D*_{t},*H*_{t-1}), a few models
were tested.
One approach is to estimate
the probability of the user to pick an image *X*_{a} from
by

and, in the case of choosing any number of images, to assume that each image
is selected independently acording to a
*p*_{softmin}.
While older versions were displaying the *n*_{t} images with the highest probability of
being the target, in the newer version the images selected for display are determined
by minimizing the expected number of future iterations estimated by entropy.
The system was tested with a database gathering images from 45 CD's of Corel stock
photographs.

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*Remco Veltkamp*

*2001-03-08*