77,910 research outputs found
Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations
This paper presents a co-clustering technique that, given a collection of
images and their hierarchies, clusters nodes from these hierarchies to obtain a
coherent multiresolution representation of the image collection. We formalize
the co-clustering as a Quadratic Semi-Assignment Problem and solve it with a
linear programming relaxation approach that makes effective use of information
from hierarchies. Initially, we address the problem of generating an optimal,
coherent partition per image and, afterwards, we extend this method to a
multiresolution framework. Finally, we particularize this framework to an
iterative multiresolution video segmentation algorithm in sequences with small
variations. We evaluate the algorithm on the Video Occlusion/Object Boundary
Detection Dataset, showing that it produces state-of-the-art results in these
scenarios.Comment: International Conference on Computer Vision (ICCV) 201
Co-interest Person Detection from Multiple Wearable Camera Videos
Wearable cameras, such as Google Glass and Go Pro, enable video data
collection over larger areas and from different views. In this paper, we tackle
a new problem of locating the co-interest person (CIP), i.e., the one who draws
attention from most camera wearers, from temporally synchronized videos taken
by multiple wearable cameras. Our basic idea is to exploit the motion patterns
of people and use them to correlate the persons across different videos,
instead of performing appearance-based matching as in traditional video
co-segmentation/localization. This way, we can identify CIP even if a group of
people with similar appearance are present in the view. More specifically, we
detect a set of persons on each frame as the candidates of the CIP and then
build a Conditional Random Field (CRF) model to select the one with consistent
motion patterns in different videos and high spacial-temporal consistency in
each video. We collect three sets of wearable-camera videos for testing the
proposed algorithm. All the involved people have similar appearances in the
collected videos and the experiments demonstrate the effectiveness of the
proposed algorithm.Comment: ICCV 201
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