528 research outputs found
Kernel Belief Propagation
We propose a nonparametric generalization of belief propagation, Kernel
Belief Propagation (KBP), for pairwise Markov random fields. Messages are
represented as functions in a reproducing kernel Hilbert space (RKHS), and
message updates are simple linear operations in the RKHS. KBP makes none of the
assumptions commonly required in classical BP algorithms: the variables need
not arise from a finite domain or a Gaussian distribution, nor must their
relations take any particular parametric form. Rather, the relations between
variables are represented implicitly, and are learned nonparametrically from
training data. KBP has the advantage that it may be used on any domain where
kernels are defined (Rd, strings, groups), even where explicit parametric
models are not known, or closed form expressions for the BP updates do not
exist. The computational cost of message updates in KBP is polynomial in the
training data size. We also propose a constant time approximate message update
procedure by representing messages using a small number of basis functions. In
experiments, we apply KBP to image denoising, depth prediction from still
images, and protein configuration prediction: KBP is faster than competing
classical and nonparametric approaches (by orders of magnitude, in some cases),
while providing significantly more accurate results
A Survey on Feature Analysis and Classification for Image Annotation using Saliency Map
With the advances in multimedia technologies collections of digital images is growing rapidly. Due to the popularity of various digital cameras and the rapid growth of social media tools, internet based photo sharing have increase in daily life. As the database have huge amount of images and other files, it is difficult to retrieve the required images. Supervised dictionary learning feature based image retrieval is very important area of research in the field of image retrieval. The feature based image annotation paradigm aims to tackle the automated image annotation by exploiting feature based image retrieval. Aim of this research work is to extract features related to the image in the form of annotation and develop system for clustering of data user define clusters with the help of image processing. To solve the problem of data classification into large dataset, to get an efficient system which classify data not only on basis of the dataset but also on basis of the image specified class
End-to-End Supervised Multilabel Contrastive Learning
Multilabel representation learning is recognized as a challenging problem
that can be associated with either label dependencies between object categories
or data-related issues such as the inherent imbalance of positive/negative
samples. Recent advances address these challenges from model- and data-centric
viewpoints. In model-centric, the label correlation is obtained by an external
model designs (e.g., graph CNN) to incorporate an inductive bias for training.
However, they fail to design an end-to-end training framework, leading to high
computational complexity. On the contrary, in data-centric, the realistic
nature of the dataset is considered for improving the classification while
ignoring the label dependencies. In this paper, we propose a new end-to-end
training framework -- dubbed KMCL (Kernel-based Mutlilabel Contrastive
Learning) -- to address the shortcomings of both model- and data-centric
designs. The KMCL first transforms the embedded features into a mixture of
exponential kernels in Gaussian RKHS. It is then followed by encoding an
objective loss that is comprised of (a) reconstruction loss to reconstruct
kernel representation, (b) asymmetric classification loss to address the
inherent imbalance problem, and (c) contrastive loss to capture label
correlation. The KMCL models the uncertainty of the feature encoder while
maintaining a low computational footprint. Extensive experiments are conducted
on image classification tasks to showcase the consistent improvements of KMCL
over the SOTA methods. PyTorch implementation is provided in
\url{https://github.com/mahdihosseini/KMCL}
Non-Convex and Geometric Methods for Tomography and Label Learning
Data labeling is a fundamental problem of mathematical data analysis in which each data point is assigned exactly one single label (prototype) from a finite predefined set. In this thesis we study two challenging extensions, where either the input data cannot be observed directly or prototypes are not available beforehand.
The main application of the first setting is discrete tomography. We propose several non-convex variational as well as smooth geometric approaches to joint image label assignment and reconstruction from indirect measurements with known prototypes. In particular, we consider spatial regularization of assignments, based on the KL-divergence, which takes into account the smooth geometry of discrete probability distributions endowed with the Fisher-Rao (information) metric, i.e. the assignment manifold. Finally, the geometric point of view leads to a smooth flow evolving on a Riemannian submanifold including the tomographic projection constraints directly into the geometry of assignments. Furthermore we investigate corresponding implicit numerical schemes which amount to solving a sequence of convex problems.
Likewise, for the second setting, when the prototypes are absent, we introduce and study a smooth dynamical system for unsupervised data labeling which evolves by geometric integration on the assignment manifold. Rigorously abstracting from ``data-label'' to ``data-data'' decisions leads to interpretable low-rank data representations, which themselves are parameterized by label assignments. The resulting self-assignment flow simultaneously performs learning of latent prototypes in the very same framework while they are used for inference. Moreover, a single parameter, the scale of regularization in terms of spatial context, drives the entire process. By smooth geodesic interpolation between different normalizations of self-assignment matrices on the positive definite matrix manifold, a one-parameter family of self-assignment flows is defined. Accordingly, the proposed approach can be characterized from different viewpoints such as discrete optimal transport, normalized spectral cuts and combinatorial optimization by completely positive factorizations, each with additional built-in spatial regularization
Multi-Label Learning with Label Enhancement
The task of multi-label learning is to predict a set of relevant labels for
the unseen instance. Traditional multi-label learning algorithms treat each
class label as a logical indicator of whether the corresponding label is
relevant or irrelevant to the instance, i.e., +1 represents relevant to the
instance and -1 represents irrelevant to the instance. Such label represented
by -1 or +1 is called logical label. Logical label cannot reflect different
label importance. However, for real-world multi-label learning problems, the
importance of each possible label is generally different. For the real
applications, it is difficult to obtain the label importance information
directly. Thus we need a method to reconstruct the essential label importance
from the logical multilabel data. To solve this problem, we assume that each
multi-label instance is described by a vector of latent real-valued labels,
which can reflect the importance of the corresponding labels. Such label is
called numerical label. The process of reconstructing the numerical labels from
the logical multi-label data via utilizing the logical label information and
the topological structure in the feature space is called Label Enhancement. In
this paper, we propose a novel multi-label learning framework called LEMLL,
i.e., Label Enhanced Multi-Label Learning, which incorporates regression of the
numerical labels and label enhancement into a unified framework. Extensive
comparative studies validate that the performance of multi-label learning can
be improved significantly with label enhancement and LEMLL can effectively
reconstruct latent label importance information from logical multi-label data.Comment: ICDM 201
Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model
Multi-label classification is the challenging task of predicting the presence
and absence of multiple targets, involving representation learning and label
correlation modeling. We propose a novel framework for multi-label
classification, Multivariate Probit Variational AutoEncoder (MPVAE), that
effectively learns latent embedding spaces as well as label correlations. MPVAE
learns and aligns two probabilistic embedding spaces for labels and features
respectively. The decoder of MPVAE takes in the samples from the embedding
spaces and models the joint distribution of output targets under a Multivariate
Probit model by learning a shared covariance matrix. We show that MPVAE
outperforms the existing state-of-the-art methods on a variety of application
domains, using public real-world datasets. MPVAE is further shown to remain
robust under noisy settings. Lastly, we demonstrate the interpretability of the
learned covariance by a case study on a bird observation dataset
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