1,734 research outputs found
Interactive multiple object learning with scanty human supervision
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/We present a fast and online human-robot interaction approach that progressively learns multiple object classifiers using scanty human supervision. Given an input video stream recorded during the human robot interaction, the user just needs to annotate a small fraction of frames to compute object specific classifiers based on random ferns which share the same features. The resulting methodology is fast (in a few seconds, complex object appearances can be learned), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier.; We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in indoor and outdoor scenarios containing a multitude of different objects. We show that with little human assistance, we are able to build object classifiers robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds. (C) 2016 Elsevier Inc. All rights reserved.Peer ReviewedPostprint (author's final draft
Automating the construction of scene classifiers for content-based video retrieval
This paper introduces a real time automatic scene classifier within content-based video retrieval. In our envisioned approach end users like documentalists, not image processing experts, build classifiers interactively, by simply indicating positive examples of a scene. Classification consists of a two stage procedure. First, small image fragments called patches are classified. Second, frequency vectors of these patch classifications are fed into a second classifier for global scene classification (e.g., city, portraits, or countryside). The first stage classifiers can be seen as a set of highly specialized, learned feature detectors, as an alternative to letting an image processing expert determine features a priori. We present results for experiments on a variety of patch and image classes. The scene classifier has been used successfully within television archives and for Internet porn filtering
LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees
Systems based on artificial intelligence and machine learning models should
be transparent, in the sense of being capable of explaining their decisions to
gain humans' approval and trust. While there are a number of explainability
techniques that can be used to this end, many of them are only capable of
outputting a single one-size-fits-all explanation that simply cannot address
all of the explainees' diverse needs. In this work we introduce a
model-agnostic and post-hoc local explainability technique for black-box
predictions called LIMEtree, which employs surrogate multi-output regression
trees. We validate our algorithm on a deep neural network trained for object
detection in images and compare it against Local Interpretable Model-agnostic
Explanations (LIME). Our method comes with local fidelity guarantees and can
produce a range of diverse explanation types, including contrastive and
counterfactual explanations praised in the literature. Some of these
explanations can be interactively personalised to create bespoke, meaningful
and actionable insights into the model's behaviour. While other methods may
give an illusion of customisability by wrapping, otherwise static, explanations
in an interactive interface, our explanations are truly interactive, in the
sense of allowing the user to "interrogate" a black-box model. LIMEtree can
therefore produce consistent explanations on which an interactive exploratory
process can be built
Training an adaptive dialogue policy for interactive learning of visually grounded word meanings
We present a multi-modal dialogue system for interactive learning of
perceptually grounded word meanings from a human tutor. The system integrates
an incremental, semantic parsing/generation framework - Dynamic Syntax and Type
Theory with Records (DS-TTR) - with a set of visual classifiers that are
learned throughout the interaction and which ground the meaning representations
that it produces. We use this system in interaction with a simulated human
tutor to study the effects of different dialogue policies and capabilities on
the accuracy of learned meanings, learning rates, and efforts/costs to the
tutor. We show that the overall performance of the learning agent is affected
by (1) who takes initiative in the dialogues; (2) the ability to express/use
their confidence level about visual attributes; and (3) the ability to process
elliptical and incrementally constructed dialogue turns. Ultimately, we train
an adaptive dialogue policy which optimises the trade-off between classifier
accuracy and tutoring costs.Comment: 11 pages, SIGDIAL 2016 Conferenc
Guided interactive image segmentation using machine learning and color based data set clustering
We present a novel approach that combines machine learning based interactive
image segmentation using supervoxels with a clustering method for the automated
identification of similarly colored images in large data sets which enables a
guided reuse of classifiers. Our approach solves the problem of significant
color variability prevalent and often unavoidable in biological and medical
images which typically leads to deteriorated segmentation and quantification
accuracy thereby greatly reducing the necessary training effort. This increase
in efficiency facilitates the quantification of much larger numbers of images
thereby enabling interactive image analysis for recent new technological
advances in high-throughput imaging. The presented methods are applicable for
almost any image type and represent a useful tool for image analysis tasks in
general
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
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