38,231 research outputs found
A Review of Codebook Models in Patch-Based Visual Object Recognition
The codebook model-based approach, while ignoring any structural aspect in vision, nonetheless provides state-of-the-art performances on current datasets. The key role of a visual codebook is to provide a way to map the low-level features into a fixed-length vector in histogram space to which standard classifiers can be directly applied. The discriminative power of such a visual codebook determines the quality of the codebook model, whereas the size of the codebook controls the complexity of the model. Thus, the construction of a codebook is an important step which is usually done by cluster analysis. However, clustering is a process that retains regions of high density in a distribution and it follows that the resulting codebook need not have discriminant properties. This is also recognised as a computational bottleneck of such systems. In our recent work, we proposed a resource-allocating codebook, to constructing a discriminant codebook in a one-pass design procedure that slightly outperforms more traditional approaches at drastically reduced computing times. In this review we survey several approaches that have been proposed over the last decade with their use of feature detectors, descriptors, codebook construction schemes, choice of classifiers in recognising objects, and datasets that were used in evaluating the proposed methods
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
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Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
As machine learning systems move from computer-science laboratories into the
open world, their accountability becomes a high priority problem.
Accountability requires deep understanding of system behavior and its failures.
Current evaluation methods such as single-score error metrics and confusion
matrices provide aggregate views of system performance that hide important
shortcomings. Understanding details about failures is important for identifying
pathways for refinement, communicating the reliability of systems in different
settings, and for specifying appropriate human oversight and engagement.
Characterization of failures and shortcomings is particularly complex for
systems composed of multiple machine learned components. For such systems,
existing evaluation methods have limited expressiveness in describing and
explaining the relationship among input content, the internal states of system
components, and final output quality. We present Pandora, a set of hybrid
human-machine methods and tools for describing and explaining system failures.
Pandora leverages both human and system-generated observations to summarize
conditions of system malfunction with respect to the input content and system
architecture. We share results of a case study with a machine learning pipeline
for image captioning that show how detailed performance views can be beneficial
for analysis and debugging
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