1,835 research outputs found
Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
The availability of large-scale annotated image datasets and recent advances
in supervised deep learning methods enable the end-to-end derivation of
representative image features that can impact a variety of image analysis
problems. Such supervised approaches, however, are difficult to implement in
the medical domain where large volumes of labelled data are difficult to obtain
due to the complexity of manual annotation and inter- and intra-observer
variability in label assignment. We propose a new convolutional sparse kernel
network (CSKN), which is a hierarchical unsupervised feature learning framework
that addresses the challenge of learning representative visual features in
medical image analysis domains where there is a lack of annotated training
data. Our framework has three contributions: (i) We extend kernel learning to
identify and represent invariant features across image sub-patches in an
unsupervised manner. (ii) We initialise our kernel learning with a layer-wise
pre-training scheme that leverages the sparsity inherent in medical images to
extract initial discriminative features. (iii) We adapt a multi-scale spatial
pyramid pooling (SPP) framework to capture subtle geometric differences between
learned visual features. We evaluated our framework in medical image retrieval
and classification on three public datasets. Our results show that our CSKN had
better accuracy when compared to other conventional unsupervised methods and
comparable accuracy to methods that used state-of-the-art supervised
convolutional neural networks (CNNs). Our findings indicate that our
unsupervised CSKN provides an opportunity to leverage unannotated big data in
medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional
Sparse Kernel Network for Unsupervised Medical Image Analysis'). The
manuscript is available from following link
(https://doi.org/10.1016/j.media.2019.06.005
Provably scale-covariant networks from oriented quasi quadrature measures in cascade
This article presents a continuous model for hierarchical networks based on a
combination of mathematically derived models of receptive fields and
biologically inspired computations. Based on a functional model of complex
cells in terms of an oriented quasi quadrature combination of first- and
second-order directional Gaussian derivatives, we couple such primitive
computations in cascade over combinatorial expansions over image orientations.
Scale-space properties of the computational primitives are analysed and it is
shown that the resulting representation allows for provable scale and rotation
covariance. A prototype application to texture analysis is developed and it is
demonstrated that a simplified mean-reduced representation of the resulting
QuasiQuadNet leads to promising experimental results on three texture datasets.Comment: 12 pages, 3 figures, 1 tabl
A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for
solving classification tasks on graphs. This survey gives a comprehensive
overview of techniques for kernel-based graph classification developed in the
past 15 years. We describe and categorize graph kernels based on properties
inherent to their design, such as the nature of their extracted graph features,
their method of computation and their applicability to problems in practice. In
an extensive experimental evaluation, we study the classification accuracy of a
large suite of graph kernels on established benchmarks as well as new datasets.
We compare the performance of popular kernels with several baseline methods and
study the effect of applying a Gaussian RBF kernel to the metric induced by a
graph kernel. In doing so, we find that simple baselines become competitive
after this transformation on some datasets. Moreover, we study the extent to
which existing graph kernels agree in their predictions (and prediction errors)
and obtain a data-driven categorization of kernels as result. Finally, based on
our experimental results, we derive a practitioner's guide to kernel-based
graph classification
Learning and Using Taxonomies For Fast Visual Categorization
The computational complexity of current visual categorization algorithms scales linearly at best with the number of categories. The goal of classifying simultaneously N_(cat) = 10^4 - 10^5 visual categories requires sub-linear classification costs. We explore algorithms for automatically building classification trees which have, in principle, log N_(cat) complexity. We find that a greedy algorithm that recursively splits the set of categories into the two minimally confused subsets achieves 5-20 fold speedups at a small cost in classification performance. Our approach is independent of the specific classification algorithm used. A welcome by-product of our algorithm is a very reasonable taxonomy of the Caltech-256 dataset
Broadcasting Convolutional Network for Visual Relational Reasoning
In this paper, we propose the Broadcasting Convolutional Network (BCN) that
extracts key object features from the global field of an entire input image and
recognizes their relationship with local features. BCN is a simple network
module that collects effective spatial features, embeds location information
and broadcasts them to the entire feature maps. We further introduce the
Multi-Relational Network (multiRN) that improves the existing Relation Network
(RN) by utilizing the BCN module. In pixel-based relation reasoning problems,
with the help of BCN, multiRN extends the concept of `pairwise relations' in
conventional RNs to `multiwise relations' by relating each object with multiple
objects at once. This yields in O(n) complexity for n objects, which is a vast
computational gain from RNs that take O(n^2). Through experiments, multiRN has
achieved a state-of-the-art performance on CLEVR dataset, which proves the
usability of BCN on relation reasoning problems.Comment: Accepted paper at ECCV 2018. 24 page
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