3,978 research outputs found
Convolutional higher order matching pursuit
We introduce a greedy generalised convolutional algorithm to efficiently locate an unknown number of sources in a series of (possibly multidimensional) images, where each source contributes a localised and low-dimensional but otherwise variable signal to its immediate spatial neighbourhood. Our approach extends convolutional matching pursuit in two ways: first, it takes the signal generated by each source to be a variable linear combination of aligned dictionary elements; and second, it executes the pursuit in the domain of high-order multivariate cumulant statistics. The resulting algorithm adapts to varying signal and noise distributions to flexibly recover source signals in a variety of settings
Compositional Model based Fisher Vector Coding for Image Classification
Deriving from the gradient vector of a generative model of local features,
Fisher vector coding (FVC) has been identified as an effective coding method
for image classification. Most, if not all, FVC implementations employ the
Gaussian mixture model (GMM) to depict the generation process of local
features. However, the representative power of the GMM could be limited because
it essentially assumes that local features can be characterized by a fixed
number of feature prototypes and the number of prototypes is usually small in
FVC. To handle this limitation, in this paper we break the convention which
assumes that a local feature is drawn from one of few Gaussian distributions.
Instead, we adopt a compositional mechanism which assumes that a local feature
is drawn from a Gaussian distribution whose mean vector is composed as the
linear combination of multiple key components and the combination weight is a
latent random variable. In this way, we can greatly enhance the representative
power of the generative model of FVC. To implement our idea, we designed two
particular generative models with such a compositional mechanism.Comment: Fixed typos. 16 pages. Appearing in IEEE T. Pattern Analysis and
Machine Intelligence (TPAMI
Convolutional Feature Masking for Joint Object and Stuff Segmentation
The topic of semantic segmentation has witnessed considerable progress due to
the powerful features learned by convolutional neural networks (CNNs). The
current leading approaches for semantic segmentation exploit shape information
by extracting CNN features from masked image regions. This strategy introduces
artificial boundaries on the images and may impact the quality of the extracted
features. Besides, the operations on the raw image domain require to compute
thousands of networks on a single image, which is time-consuming. In this
paper, we propose to exploit shape information via masking convolutional
features. The proposal segments (e.g., super-pixels) are treated as masks on
the convolutional feature maps. The CNN features of segments are directly
masked out from these maps and used to train classifiers for recognition. We
further propose a joint method to handle objects and "stuff" (e.g., grass, sky,
water) in the same framework. State-of-the-art results are demonstrated on
benchmarks of PASCAL VOC and new PASCAL-CONTEXT, with a compelling
computational speed.Comment: IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
201
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
- …