39,874 research outputs found
Large Margin Image Set Representation and Classification
In this paper, we propose a novel image set representation and classification
method by maximizing the margin of image sets. The margin of an image set is
defined as the difference of the distance to its nearest image set from
different classes and the distance to its nearest image set of the same class.
By modeling the image sets by using both their image samples and their affine
hull models, and maximizing the margins of the images sets, the image set
representation parameter learning problem is formulated as an minimization
problem, which is further optimized by an expectation -maximization (EM)
strategy with accelerated proximal gradient (APG) optimization in an iterative
algorithm. To classify a given test image set, we assign it to the class which
could provide the largest margin. Experiments on two applications of
video-sequence-based face recognition demonstrate that the proposed method
significantly outperforms state-of-the-art image set classification methods in
terms of both effectiveness and efficiency
Simple to Complex Cross-modal Learning to Rank
The heterogeneity-gap between different modalities brings a significant
challenge to multimedia information retrieval. Some studies formalize the
cross-modal retrieval tasks as a ranking problem and learn a shared multi-modal
embedding space to measure the cross-modality similarity. However, previous
methods often establish the shared embedding space based on linear mapping
functions which might not be sophisticated enough to reveal more complicated
inter-modal correspondences. Additionally, current studies assume that the
rankings are of equal importance, and thus all rankings are used
simultaneously, or a small number of rankings are selected randomly to train
the embedding space at each iteration. Such strategies, however, always suffer
from outliers as well as reduced generalization capability due to their lack of
insightful understanding of procedure of human cognition. In this paper, we
involve the self-paced learning theory with diversity into the cross-modal
learning to rank and learn an optimal multi-modal embedding space based on
non-linear mapping functions. This strategy enhances the model's robustness to
outliers and achieves better generalization via training the model gradually
from easy rankings by diverse queries to more complex ones. An efficient
alternative algorithm is exploited to solve the proposed challenging problem
with fast convergence in practice. Extensive experimental results on several
benchmark datasets indicate that the proposed method achieves significant
improvements over the state-of-the-arts in this literature.Comment: 14 pages; Accepted by Computer Vision and Image Understandin
Supervised Dictionary Learning
It is now well established that sparse signal models are well suited to
restoration tasks and can effectively be learned from audio, image, and video
data. Recent research has been aimed at learning discriminative sparse models
instead of purely reconstructive ones. This paper proposes a new step in that
direction, with a novel sparse representation for signals belonging to
different classes in terms of a shared dictionary and multiple class-decision
functions. The linear variant of the proposed model admits a simple
probabilistic interpretation, while its most general variant admits an
interpretation in terms of kernels. An optimization framework for learning all
the components of the proposed model is presented, along with experimental
results on standard handwritten digit and texture classification tasks
Nonparametric Feature Extraction from Dendrograms
We propose feature extraction from dendrograms in a nonparametric way. The
Minimax distance measures correspond to building a dendrogram with single
linkage criterion, with defining specific forms of a level function and a
distance function over that. Therefore, we extend this method to arbitrary
dendrograms. We develop a generalized framework wherein different distance
measures can be inferred from different types of dendrograms, level functions
and distance functions. Via an appropriate embedding, we compute a vector-based
representation of the inferred distances, in order to enable many numerical
machine learning algorithms to employ such distances. Then, to address the
model selection problem, we study the aggregation of different dendrogram-based
distances respectively in solution space and in representation space in the
spirit of deep representations. In the first approach, for example for the
clustering problem, we build a graph with positive and negative edge weights
according to the consistency of the clustering labels of different objects
among different solutions, in the context of ensemble methods. Then, we use an
efficient variant of correlation clustering to produce the final clusters. In
the second approach, we investigate the sequential combination of different
distances and features sequentially in the spirit of multi-layered
architectures to obtain the final features. Finally, we demonstrate the
effectiveness of our approach via several numerical studies
Clustering Memes in Social Media
The increasing pervasiveness of social media creates new opportunities to
study human social behavior, while challenging our capability to analyze their
massive data streams. One of the emerging tasks is to distinguish between
different kinds of activities, for example engineered misinformation campaigns
versus spontaneous communication. Such detection problems require a formal
definition of meme, or unit of information that can spread from person to
person through the social network. Once a meme is identified, supervised
learning methods can be applied to classify different types of communication.
The appropriate granularity of a meme, however, is hardly captured from
existing entities such as tags and keywords. Here we present a framework for
the novel task of detecting memes by clustering messages from large streams of
social data. We evaluate various similarity measures that leverage content,
metadata, network features, and their combinations. We also explore the idea of
pre-clustering on the basis of existing entities. A systematic evaluation is
carried out using a manually curated dataset as ground truth. Our analysis
shows that pre-clustering and a combination of heterogeneous features yield the
best trade-off between number of clusters and their quality, demonstrating that
a simple combination based on pairwise maximization of similarity is as
effective as a non-trivial optimization of parameters. Our approach is fully
automatic, unsupervised, and scalable for real-time detection of memes in
streaming data.Comment: Proceedings of the 2013 IEEE/ACM International Conference on Advances
in Social Networks Analysis and Mining (ASONAM'13), 201
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