5,310 research outputs found
LogDet Rank Minimization with Application to Subspace Clustering
Low-rank matrix is desired in many machine learning and computer vision
problems. Most of the recent studies use the nuclear norm as a convex surrogate
of the rank operator. However, all singular values are simply added together by
the nuclear norm, and thus the rank may not be well approximated in practical
problems. In this paper, we propose to use a log-determinant (LogDet) function
as a smooth and closer, though non-convex, approximation to rank for obtaining
a low-rank representation in subspace clustering. Augmented Lagrange
multipliers strategy is applied to iteratively optimize the LogDet-based
non-convex objective function on potentially large-scale data. By making use of
the angular information of principal directions of the resultant low-rank
representation, an affinity graph matrix is constructed for spectral
clustering. Experimental results on motion segmentation and face clustering
data demonstrate that the proposed method often outperforms state-of-the-art
subspace clustering algorithms.Comment: 10 pages, 4 figure
Self-Selective Correlation Ship Tracking Method for Smart Ocean System
In recent years, with the development of the marine industry, navigation
environment becomes more complicated. Some artificial intelligence
technologies, such as computer vision, can recognize, track and count the
sailing ships to ensure the maritime security and facilitates the management
for Smart Ocean System. Aiming at the scaling problem and boundary effect
problem of traditional correlation filtering methods, we propose a
self-selective correlation filtering method based on box regression (BRCF). The
proposed method mainly include: 1) A self-selective model with negative samples
mining method which effectively reduces the boundary effect in strengthening
the classification ability of classifier at the same time; 2) A bounding box
regression method combined with a key points matching method for the scale
prediction, leading to a fast and efficient calculation. The experimental
results show that the proposed method can effectively deal with the problem of
ship size changes and background interference. The success rates and precisions
were higher than Discriminative Scale Space Tracking (DSST) by over 8
percentage points on the marine traffic dataset of our laboratory. In terms of
processing speed, the proposed method is higher than DSST by nearly 22 Frames
Per Second (FPS)
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