570 research outputs found
Fast Approximate -Means via Cluster Closures
-means, a simple and effective clustering algorithm, is one of the most
widely used algorithms in multimedia and computer vision community. Traditional
-means is an iterative algorithm---in each iteration new cluster centers are
computed and each data point is re-assigned to its nearest center. The cluster
re-assignment step becomes prohibitively expensive when the number of data
points and cluster centers are large.
In this paper, we propose a novel approximate -means algorithm to greatly
reduce the computational complexity in the assignment step. Our approach is
motivated by the observation that most active points changing their cluster
assignments at each iteration are located on or near cluster boundaries. The
idea is to efficiently identify those active points by pre-assembling the data
into groups of neighboring points using multiple random spatial partition
trees, and to use the neighborhood information to construct a closure for each
cluster, in such a way only a small number of cluster candidates need to be
considered when assigning a data point to its nearest cluster. Using complexity
analysis, image data clustering, and applications to image retrieval, we show
that our approach out-performs state-of-the-art approximate -means
algorithms in terms of clustering quality and efficiency
Low-rank SIFT: An Affine Invariant Feature for Place Recognition
In this paper, we present a novel affine-invariant feature based on SIFT,
leveraging the regular appearance of man-made objects. The feature achieves
full affine invariance without needing to simulate over affine parameter space.
Low-rank SIFT, as we name the feature, is based on our observation that local
tilt, which are caused by changes of camera axis orientation, could be
normalized by converting local patches to standard low-rank forms. Rotation,
translation and scaling invariance could be achieved in ways similar to SIFT.
As an extension of SIFT, our method seeks to add prior to solve the ill-posed
affine parameter estimation problem and normalizes them directly, and is
applicable to objects with regular structures. Furthermore, owing to recent
breakthrough in convex optimization, such parameter could be computed
efficiently. We will demonstrate its effectiveness in place recognition as our
major application. As extra contributions, we also describe our pipeline of
constructing geotagged building database from the ground up, as well as an
efficient scheme for automatic feature selection
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