42,939 research outputs found
Proximal Methods for Hierarchical Sparse Coding
Sparse coding consists in representing signals as sparse linear combinations
of atoms selected from a dictionary. We consider an extension of this framework
where the atoms are further assumed to be embedded in a tree. This is achieved
using a recently introduced tree-structured sparse regularization norm, which
has proven useful in several applications. This norm leads to regularized
problems that are difficult to optimize, and we propose in this paper efficient
algorithms for solving them. More precisely, we show that the proximal operator
associated with this norm is computable exactly via a dual approach that can be
viewed as the composition of elementary proximal operators. Our procedure has a
complexity linear, or close to linear, in the number of atoms, and allows the
use of accelerated gradient techniques to solve the tree-structured sparse
approximation problem at the same computational cost as traditional ones using
the L1-norm. Our method is efficient and scales gracefully to millions of
variables, which we illustrate in two types of applications: first, we consider
fixed hierarchical dictionaries of wavelets to denoise natural images. Then, we
apply our optimization tools in the context of dictionary learning, where
learned dictionary elements naturally organize in a prespecified arborescent
structure, leading to a better performance in reconstruction of natural image
patches. When applied to text documents, our method learns hierarchies of
topics, thus providing a competitive alternative to probabilistic topic models
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling
We frame the task of predicting a semantic labeling as a sparse
reconstruction procedure that applies a target-specific learned transfer
function to a generic deep sparse code representation of an image. This
strategy partitions training into two distinct stages. First, in an
unsupervised manner, we learn a set of generic dictionaries optimized for
sparse coding of image patches. We train a multilayer representation via
recursive sparse dictionary learning on pooled codes output by earlier layers.
Second, we encode all training images with the generic dictionaries and learn a
transfer function that optimizes reconstruction of patches extracted from
annotated ground-truth given the sparse codes of their corresponding image
patches. At test time, we encode a novel image using the generic dictionaries
and then reconstruct using the transfer function. The output reconstruction is
a semantic labeling of the test image.
Applying this strategy to the task of contour detection, we demonstrate
performance competitive with state-of-the-art systems. Unlike almost all prior
work, our approach obviates the need for any form of hand-designed features or
filters. To illustrate general applicability, we also show initial results on
semantic part labeling of human faces.
The effectiveness of our approach opens new avenues for research on deep
sparse representations. Our classifiers utilize this representation in a novel
manner. Rather than acting on nodes in the deepest layer, they attach to nodes
along a slice through multiple layers of the network in order to make
predictions about local patches. Our flexible combination of a generatively
learned sparse representation with discriminatively trained transfer
classifiers extends the notion of sparse reconstruction to encompass arbitrary
semantic labeling tasks.Comment: to appear in Asian Conference on Computer Vision (ACCV), 201
Multi-task Image Classification via Collaborative, Hierarchical Spike-and-Slab Priors
Promising results have been achieved in image classification problems by
exploiting the discriminative power of sparse representations for
classification (SRC). Recently, it has been shown that the use of
\emph{class-specific} spike-and-slab priors in conjunction with the
class-specific dictionaries from SRC is particularly effective in low training
scenarios. As a logical extension, we build on this framework for multitask
scenarios, wherein multiple representations of the same physical phenomena are
available. We experimentally demonstrate the benefits of mining joint
information from different camera views for multi-view face recognition.Comment: Accepted to International Conference in Image Processing (ICIP) 201
Multimodal Multipart Learning for Action Recognition in Depth Videos
The articulated and complex nature of human actions makes the task of action
recognition difficult. One approach to handle this complexity is dividing it to
the kinetics of body parts and analyzing the actions based on these partial
descriptors. We propose a joint sparse regression based learning method which
utilizes the structured sparsity to model each action as a combination of
multimodal features from a sparse set of body parts. To represent dynamics and
appearance of parts, we employ a heterogeneous set of depth and skeleton based
features. The proper structure of multimodal multipart features are formulated
into the learning framework via the proposed hierarchical mixed norm, to
regularize the structured features of each part and to apply sparsity between
them, in favor of a group feature selection. Our experimental results expose
the effectiveness of the proposed learning method in which it outperforms other
methods in all three tested datasets while saturating one of them by achieving
perfect accuracy
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