18,139 research outputs found
A novel active learning technique for multi-label remote sensing image scene classification
Copyright 2018 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.This paper presents a novel multi-label active learning (MLAL) technique in the framework of multi-label remote sensing (RS) image scene classification problems. The proposed MLAL technique is developed in the framework of the multi-label SVM classifier (ML-SVM). Unlike the standard AL methods, the proposed MLAL technique redefines active learning by evaluating the informativeness of each image based on its multiple land-cover classes. Accordingly, the proposed MLAL technique is based on the joint evaluation of two criteria for the selection of the most informative images: i) multi-label uncertainty and ii) multi-label diversity. The multi-label uncertainty criterion is associated to the confidence of the multi-label classification algorithm in correctly assigning multi-labels to each image, whereas multi-label diversity criterion aims at selecting a set of un-annotated images that are as more diverse as possible to reduce the redundancy among them. In order to evaluate the multi-label uncertainty of each image, we propose a novel multi-label margin sampling strategy that: 1) considers the functional distances of each image to all ML-SVM hyperplanes; and then 2) estimates the occurrence on how many times each image falls inside the margins of ML-SVMs. If the occurrence is small, the classifiers are confident to correctly classify the considered image, and vice versa. In order to evaluate the multi-label diversity of each image, we propose a novel clustering-based strategy that clusters all the images inside the margins of the ML-SVMs and avoids selecting the uncertain images from the same clusters. The joint use of the two criteria allows one to enrich the training set of images with multi-labels. Experimental results obtained on a benchmark archive with 2100 images with their multi-labels show the effectiveness of the proposed MLAL method compared to the standard AL methods that neglect the evaluation of the uncertainty and diversity on multi-labels.EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEart
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks
How can we reuse existing knowledge, in the form of available datasets, when
solving a new and apparently unrelated target task from a set of unlabeled
data? In this work we make a first contribution to answer this question in the
context of image classification. We frame this quest as an active learning
problem and use zero-shot classifiers to guide the learning process by linking
the new task to the existing classifiers. By revisiting the dual formulation of
adaptive SVM, we reveal two basic conditions to choose greedily only the most
relevant samples to be annotated. On this basis we propose an effective active
learning algorithm which learns the best possible target classification model
with minimum human labeling effort. Extensive experiments on two challenging
datasets show the value of our approach compared to the state-of-the-art active
learning methodologies, as well as its potential to reuse past datasets with
minimal effort for future tasks
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems
A growing number of applications, e.g. video surveillance and medical image
analysis, require training recognition systems from large amounts of weakly
annotated data while some targeted interactions with a domain expert are
allowed to improve the training process. In such cases, active learning (AL)
can reduce labeling costs for training a classifier by querying the expert to
provide the labels of most informative instances. This paper focuses on AL
methods for instance classification problems in multiple instance learning
(MIL), where data is arranged into sets, called bags, that are weakly labeled.
Most AL methods focus on single instance learning problems. These methods are
not suitable for MIL problems because they cannot account for the bag structure
of data. In this paper, new methods for bag-level aggregation of instance
informativeness are proposed for multiple instance active learning (MIAL). The
\textit{aggregated informativeness} method identifies the most informative
instances based on classifier uncertainty, and queries bags incorporating the
most information. The other proposed method, called \textit{cluster-based
aggregative sampling}, clusters data hierarchically in the instance space. The
informativeness of instances is assessed by considering bag labels, inferred
instance labels, and the proportion of labels that remain to be discovered in
clusters. Both proposed methods significantly outperform reference methods in
extensive experiments using benchmark data from several application domains.
Results indicate that using an appropriate strategy to address MIAL problems
yields a significant reduction in the number of queries needed to achieve the
same level of performance as single instance AL methods
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Hyperspectral images show similar statistical properties to natural grayscale
or color photographic images. However, the classification of hyperspectral
images is more challenging because of the very high dimensionality of the
pixels and the small number of labeled examples typically available for
learning. These peculiarities lead to particular signal processing problems,
mainly characterized by indetermination and complex manifolds. The framework of
statistical learning has gained popularity in the last decade. New methods have
been presented to account for the spatial homogeneity of images, to include
user's interaction via active learning, to take advantage of the manifold
structure with semisupervised learning, to extract and encode invariances, or
to adapt classifiers and image representations to unseen yet similar scenes.
This tutuorial reviews the main advances for hyperspectral remote sensing image
classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201
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