Fast and scalable image retrieval using predictive clustering trees
Date Issued
2013-10-06
Author(s)
Kocev, Dragi
Džeroski, Sasho
Abstract
The recent overwhelming increase in the amount of available
visual information, especially digital images,has brought up a pressing
need to develop efficient and accurate systems for image retrieval. Stateof-the-art systems for image retrieval use the bag-of-visual-words representation of the images. However, the computational bottleneck in all
such systems is the construction of the visual vocabulary (i.e., how to
obtain the visual words). This is typically performed by clustering hundreds of thousands or millions of local descriptors, where the resulting
clusters correspond to visual words. Each image is then represented by
a histogram of the distribution of its local descriptors throughout the
vocabulary. The major issue in the retrieval systems is that by increasing the sizes of the image databases, the number of local descriptors to
be clustered increases rapidly: Thus, using conventional clustering techniques is infeasible. Considering this, we propose to construct the visual
codebook by using predictive clustering trees, which are very efficient
and have good performance. Moreover, to increase the stability of the
model, we propose to use random forests of predictive clustering trees.
We evaluate the proposed method on a benchmark database of a million
images and compare it to other state-of-the-art methods. The results
reveal that the proposed method produces a visual vocabulary with superior discriminative power and thus better retrieval performance.
visual information, especially digital images,has brought up a pressing
need to develop efficient and accurate systems for image retrieval. Stateof-the-art systems for image retrieval use the bag-of-visual-words representation of the images. However, the computational bottleneck in all
such systems is the construction of the visual vocabulary (i.e., how to
obtain the visual words). This is typically performed by clustering hundreds of thousands or millions of local descriptors, where the resulting
clusters correspond to visual words. Each image is then represented by
a histogram of the distribution of its local descriptors throughout the
vocabulary. The major issue in the retrieval systems is that by increasing the sizes of the image databases, the number of local descriptors to
be clustered increases rapidly: Thus, using conventional clustering techniques is infeasible. Considering this, we propose to construct the visual
codebook by using predictive clustering trees, which are very efficient
and have good performance. Moreover, to increase the stability of the
model, we propose to use random forests of predictive clustering trees.
We evaluate the proposed method on a benchmark database of a million
images and compare it to other state-of-the-art methods. The results
reveal that the proposed method produces a visual vocabulary with superior discriminative power and thus better retrieval performance.
Subjects
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