3,526 research outputs found
Face Clustering: Representation and Pairwise Constraints
Clustering face images according to their identity has two important
applications: (i) grouping a collection of face images when no external labels
are associated with images, and (ii) indexing for efficient large scale face
retrieval. The clustering problem is composed of two key parts: face
representation and choice of similarity for grouping faces. We first propose a
representation based on ResNet, which has been shown to perform very well in
image classification problems. Given this representation, we design a
clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly
estimates the adjacency matrix only based on the similarity between face
images. This allows a dynamic selection of number of clusters and retains
pairwise similarity between faces. ConPaC formulates the clustering problem as
a Conditional Random Field (CRF) model and uses Loopy Belief Propagation to
find an approximate solution for maximizing the posterior probability of the
adjacency matrix. Experimental results on two benchmark face datasets (LFW and
IJB-B) show that ConPaC outperforms well known clustering algorithms such as
k-means, spectral clustering and approximate rank-order. Additionally, our
algorithm can naturally incorporate pairwise constraints to obtain a
semi-supervised version that leads to improved clustering performance. We also
propose an k-NN variant of ConPaC, which has a linear time complexity given a
k-NN graph, suitable for large datasets.Comment: This second version is the same as TIFS version. Some experiment
results are different from v1 because we correct the protocol
Pairwise Constraint Propagation on Multi-View Data
This paper presents a graph-based learning approach to pairwise constraint
propagation on multi-view data. Although pairwise constraint propagation has
been studied extensively, pairwise constraints are usually defined over pairs
of data points from a single view, i.e., only intra-view constraint propagation
is considered for multi-view tasks. In fact, very little attention has been
paid to inter-view constraint propagation, which is more challenging since
pairwise constraints are now defined over pairs of data points from different
views. In this paper, we propose to decompose the challenging inter-view
constraint propagation problem into semi-supervised learning subproblems so
that they can be efficiently solved based on graph-based label propagation. To
the best of our knowledge, this is the first attempt to give an efficient
solution to inter-view constraint propagation from a semi-supervised learning
viewpoint. Moreover, since graph-based label propagation has been adopted for
basic optimization, we develop two constrained graph construction methods for
interview constraint propagation, which only differ in how the intra-view
pairwise constraints are exploited. The experimental results in cross-view
retrieval have shown the promising performance of our inter-view constraint
propagation
Dominant Sets for "Constrained" Image Segmentation
Image segmentation has come a long way since the early days of computer
vision, and still remains a challenging task. Modern variations of the
classical (purely bottom-up) approach, involve, e.g., some form of user
assistance (interactive segmentation) or ask for the simultaneous segmentation
of two or more images (co-segmentation). At an abstract level, all these
variants can be thought of as "constrained" versions of the original
formulation, whereby the segmentation process is guided by some external source
of information. In this paper, we propose a new approach to tackle this kind of
problems in a unified way. Our work is based on some properties of a family of
quadratic optimization problems related to dominant sets, a well-known
graph-theoretic notion of a cluster which generalizes the concept of a maximal
clique to edge-weighted graphs. In particular, we show that by properly
controlling a regularization parameter which determines the structure and the
scale of the underlying problem, we are in a position to extract groups of
dominant-set clusters that are constrained to contain predefined elements. In
particular, we shall focus on interactive segmentation and co-segmentation (in
both the unsupervised and the interactive versions). The proposed algorithm can
deal naturally with several type of constraints and input modality, including
scribbles, sloppy contours, and bounding boxes, and is able to robustly handle
noisy annotations on the part of the user. Experiments on standard benchmark
datasets show the effectiveness of our approach as compared to state-of-the-art
algorithms on a variety of natural images under several input conditions and
constraints.Comment: arXiv admin note: text overlap with arXiv:1608.0064
Volumetric Super-Resolution of Multispectral Data
Most multispectral remote sensors (e.g. QuickBird, IKONOS, and Landsat 7
ETM+) provide low-spatial high-spectral resolution multispectral (MS) or
high-spatial low-spectral resolution panchromatic (PAN) images, separately. In
order to reconstruct a high-spatial/high-spectral resolution multispectral
image volume, either the information in MS and PAN images are fused (i.e.
pansharpening) or super-resolution reconstruction (SRR) is used with only MS
images captured on different dates. Existing methods do not utilize temporal
information of MS and high spatial resolution of PAN images together to improve
the resolution. In this paper, we propose a multiframe SRR algorithm using
pansharpened MS images, taking advantage of both temporal and spatial
information available in multispectral imagery, in order to exceed spatial
resolution of given PAN images. We first apply pansharpening to a set of
multispectral images and their corresponding PAN images captured on different
dates. Then, we use the pansharpened multispectral images as input to the
proposed wavelet-based multiframe SRR method to yield full volumetric SRR. The
proposed SRR method is obtained by deriving the subband relations between
multitemporal MS volumes. We demonstrate the results on Landsat 7 ETM+ images
comparing our method to conventional techniques.Comment: arXiv admin note: text overlap with arXiv:1705.0125
All the people around me: face discovery in egocentric photo-streams
Given an unconstrained stream of images captured by a wearable photo-camera
(2fpm), we propose an unsupervised bottom-up approach for automatic clustering
appearing faces into the individual identities present in these data. The
problem is challenging since images are acquired under real world conditions;
hence the visible appearance of the people in the images undergoes intensive
variations. Our proposed pipeline consists of first arranging the photo-stream
into events, later, localizing the appearance of multiple people in them, and
finally, grouping various appearances of the same person across different
events. Experimental results performed on a dataset acquired by wearing a
photo-camera during one month, demonstrate the effectiveness of the proposed
approach for the considered purpose.Comment: 5 pages, 3 figures, accepted in IEEE International Conference on
Image Processing (ICIP 2017
End-to-end Face Detection and Cast Grouping in Movies Using Erd\H{o}s-R\'{e}nyi Clustering
We present an end-to-end system for detecting and clustering faces by
identity in full-length movies. Unlike works that start with a predefined set
of detected faces, we consider the end-to-end problem of detection and
clustering together. We make three separate contributions. First, we combine a
state-of-the-art face detector with a generic tracker to extract high quality
face tracklets. We then introduce a novel clustering method, motivated by the
classic graph theory results of Erd\H{o}s and R\'enyi. It is based on the
observations that large clusters can be fully connected by joining just a small
fraction of their point pairs, while just a single connection between two
different people can lead to poor clustering results. This suggests clustering
using a verification system with very few false positives but perhaps moderate
recall. We introduce a novel verification method, rank-1 counts verification,
that has this property, and use it in a link-based clustering scheme. Finally,
we define a novel end-to-end detection and clustering evaluation metric
allowing us to assess the accuracy of the entire end-to-end system. We present
state-of-the-art results on multiple video data sets and also on standard face
databases.Comment: to appear in ICCV 2017 (spotlight
A review of EO image information mining
We analyze the state of the art of content-based retrieval in Earth
observation image archives focusing on complete systems showing promise for
operational implementation. The different paradigms at the basis of the main
system families are introduced. The approaches taken are analyzed, focusing in
particular on the phases after primitive feature extraction. The solutions
envisaged for the issues related to feature simplification and synthesis,
indexing, semantic labeling are reviewed. The methodologies for query
specification and execution are analyzed
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV
Deep Clustering With Intra-class Distance Constraint for Hyperspectral Images
The high dimensionality of hyperspectral images often results in the
degradation of clustering performance. Due to the powerful ability of deep
feature extraction and non-linear feature representation, the clustering
algorithm based on deep learning has become a hot research topic in the field
of hyperspectral remote sensing. However, most deep clustering algorithms for
hyperspectral images utilize deep neural networks as feature extractor without
considering prior knowledge constraints that are suitable for clustering. To
solve this problem, we propose an intra-class distance constrained deep
clustering algorithm for high-dimensional hyperspectral images. The proposed
algorithm constrains the feature mapping procedure of the auto-encoder network
by intra-class distance so that raw images are transformed from the original
high-dimensional space to the low-dimensional feature space that is more
conducive to clustering. Furthermore, the related learning process is treated
as a joint optimization problem of deep feature extraction and clustering.
Experimental results demonstrate the intense competitiveness of the proposed
algorithm in comparison with state-of-the-art clustering methods of
hyperspectral images
Beyond Pixels: A Comprehensive Survey from Bottom-up to Semantic Image Segmentation and Cosegmentation
Image segmentation refers to the process to divide an image into
nonoverlapping meaningful regions according to human perception, which has
become a classic topic since the early ages of computer vision. A lot of
research has been conducted and has resulted in many applications. However,
while many segmentation algorithms exist, yet there are only a few sparse and
outdated summarizations available, an overview of the recent achievements and
issues is lacking. We aim to provide a comprehensive review of the recent
progress in this field. Covering 180 publications, we give an overview of broad
areas of segmentation topics including not only the classic bottom-up
approaches, but also the recent development in superpixel, interactive methods,
object proposals, semantic image parsing and image cosegmentation. In addition,
we also review the existing influential datasets and evaluation metrics.
Finally, we suggest some design flavors and research directions for future
research in image segmentation.Comment: submitted to Elsevier Journal of Visual Communications and Image
Representatio
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