2,185 research outputs found
Robust Recovery of Subspace Structures by Low-Rank Representation
In this work we address the subspace recovery problem. Given a set of data
samples (vectors) approximately drawn from a union of multiple subspaces, our
goal is to segment the samples into their respective subspaces and correct the
possible errors as well. To this end, we propose a novel method termed Low-Rank
Representation (LRR), which seeks the lowest-rank representation among all the
candidates that can represent the data samples as linear combinations of the
bases in a given dictionary. It is shown that LRR well solves the subspace
recovery problem: when the data is clean, we prove that LRR exactly captures
the true subspace structures; for the data contaminated by outliers, we prove
that under certain conditions LRR can exactly recover the row space of the
original data and detect the outlier as well; for the data corrupted by
arbitrary errors, LRR can also approximately recover the row space with
theoretical guarantees. Since the subspace membership is provably determined by
the row space, these further imply that LRR can perform robust subspace
segmentation and error correction, in an efficient way.Comment: IEEE Trans. Pattern Analysis and Machine Intelligenc
Watch and Learn: Semi-Supervised Learning of Object Detectors from Videos
We present a semi-supervised approach that localizes multiple unknown object
instances in long videos. We start with a handful of labeled boxes and
iteratively learn and label hundreds of thousands of object instances. We
propose criteria for reliable object detection and tracking for constraining
the semi-supervised learning process and minimizing semantic drift. Our
approach does not assume exhaustive labeling of each object instance in any
single frame, or any explicit annotation of negative data. Working in such a
generic setting allow us to tackle multiple object instances in video, many of
which are static. In contrast, existing approaches either do not consider
multiple object instances per video, or rely heavily on the motion of the
objects present. The experiments demonstrate the effectiveness of our approach
by evaluating the automatically labeled data on a variety of metrics like
quality, coverage (recall), diversity, and relevance to training an object
detector.Comment: To appear in CVPR 201
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
Skin lesion segmentation is a fundamental task in dermoscopic image analysis.
The complex features of pixels in the lesion region impede the lesion
segmentation accuracy, and existing deep learning-based methods often lack
interpretability to this problem. In this work, we propose a novel unsupervised
Skin Lesion sEgmentation framework based on structural entropy and isolation
forest outlier Detection, namely SLED. Specifically, skin lesions are segmented
by minimizing the structural entropy of a superpixel graph constructed from the
dermoscopic image. Then, we characterize the consistency of healthy skin
features and devise a novel multi-scale segmentation mechanism by outlier
detection, which enhances the segmentation accuracy by leveraging the
superpixel features from multiple scales. We conduct experiments on four skin
lesion benchmarks and compare SLED with nine representative unsupervised
segmentation methods. Experimental results demonstrate the superiority of the
proposed framework. Additionally, some case studies are analyzed to demonstrate
the effectiveness of SLED.Comment: 10 pages, 8 figures, conference. Accepted by IEEE ICDM 202
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