6,485 research outputs found
Deep filter banks for texture recognition, description, and segmentation
Visual textures have played a key role in image understanding because they
convey important semantics of images, and because texture representations that
pool local image descriptors in an orderless manner have had a tremendous
impact in diverse applications. In this paper we make several contributions to
texture understanding. First, instead of focusing on texture instance and
material category recognition, we propose a human-interpretable vocabulary of
texture attributes to describe common texture patterns, complemented by a new
describable texture dataset for benchmarking. Second, we look at the problem of
recognizing materials and texture attributes in realistic imaging conditions,
including when textures appear in clutter, developing corresponding benchmarks
on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic
texture representations, including bag-of-visual-words and the Fisher vectors,
in the context of deep learning and show that these have excellent efficiency
and generalization properties if the convolutional layers of a deep model are
used as filter banks. We obtain in this manner state-of-the-art performance in
numerous datasets well beyond textures, an efficient method to apply deep
features to image regions, as well as benefit in transferring features from one
domain to another.Comment: 29 pages; 13 figures; 8 table
DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition
Being symmetric positive-definite (SPD), covariance matrix has traditionally
been used to represent a set of local descriptors in visual recognition. Recent
study shows that kernel matrix can give considerably better representation by
modelling the nonlinearity in the local descriptor set. Nevertheless, neither
the descriptors nor the kernel matrix is deeply learned. Worse, they are
considered separately, hindering the pursuit of an optimal SPD representation.
This work proposes a deep network that jointly learns local descriptors,
kernel-matrix-based SPD representation, and the classifier via an end-to-end
training process. We derive the derivatives for the mapping from a local
descriptor set to the SPD representation to carry out backpropagation. Also, we
exploit the Daleckii-Krein formula in operator theory to give a concise and
unified result on differentiating SPD matrix functions, including the matrix
logarithm to handle the Riemannian geometry of kernel matrix. Experiments not
only show the superiority of kernel-matrix-based SPD representation with deep
local descriptors, but also verify the advantage of the proposed deep network
in pursuing better SPD representations for fine-grained image recognition
tasks
- …