16,222 research outputs found
Multi-scale Discriminant Saliency with Wavelet-based Hidden Markov Tree Modelling
The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between centre and surround
classes. Discriminant power of features for the classification is measured as
mutual information between distributions of image features and corresponding
classes . As the estimated discrepancy very much depends on considered scale
level, multi-scale structure and discriminant power are integrated by employing
discrete wavelet features and Hidden Markov Tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, a saliency value for
each square block at each scale level is computed with discriminant power
principle. Finally, across multiple scales is integrated the final saliency map
by an information maximization rule. Both standard quantitative tools such as
NSS, LCC, AUC and qualitative assessments are used for evaluating the proposed
multi-scale discriminant saliency (MDIS) method against the well-know
information based approach AIM on its released image collection with
eye-tracking data. Simulation results are presented and analysed to verify the
validity of MDIS as well as point out its limitation for further research
direction.Comment: arXiv admin note: substantial text overlap with arXiv:1301.396
Multiscale Discriminant Saliency for Visual Attention
The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between center and surround
classes. Discriminant power of features for the classification is measured as
mutual information between features and two classes distribution. The estimated
discrepancy of two feature classes very much depends on considered scale
levels; then, multi-scale structure and discriminant power are integrated by
employing discrete wavelet features and Hidden markov tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, saliency value for
each dyadic square at each scale level is computed with discriminant power
principle and the MAP. Finally, across multiple scales is integrated the final
saliency map by an information maximization rule. Both standard quantitative
tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating
the proposed multiscale discriminant saliency method (MDIS) against the
well-know information-based saliency method AIM on its Bruce Database wity
eye-tracking data. Simulation results are presented and analyzed to verify the
validity of MDIS as well as point out its disadvantages for further research
direction.Comment: 16 pages, ICCSA 2013 - BIOCA sessio
Distinguishing Hidden Markov Chains
Hidden Markov Chains (HMCs) are commonly used mathematical models of
probabilistic systems. They are employed in various fields such as speech
recognition, signal processing, and biological sequence analysis. We consider
the problem of distinguishing two given HMCs based on an observation sequence
that one of the HMCs generates. More precisely, given two HMCs and an
observation sequence, a distinguishing algorithm is expected to identify the
HMC that generates the observation sequence. Two HMCs are called
distinguishable if for every there is a distinguishing
algorithm whose error probability is less than . We show that one
can decide in polynomial time whether two HMCs are distinguishable. Further, we
present and analyze two distinguishing algorithms for distinguishable HMCs. The
first algorithm makes a decision after processing a fixed number of
observations, and it exhibits two-sided error. The second algorithm processes
an unbounded number of observations, but the algorithm has only one-sided
error. The error probability, for both algorithms, decays exponentially with
the number of processed observations. We also provide an algorithm for
distinguishing multiple HMCs. Finally, we discuss an application in stochastic
runtime verification.Comment: This is the full version of a LICS'16 pape
General anesthesia reduces complexity and temporal asymmetry of the informational structures derived from neural recordings in Drosophila
We apply techniques from the field of computational mechanics to evaluate the
statistical complexity of neural recording data from fruit flies. First, we
connect statistical complexity to the flies' level of conscious arousal, which
is manipulated by general anesthesia (isoflurane). We show that the complexity
of even single channel time series data decreases under anesthesia. The
observed difference in complexity between the two states of conscious arousal
increases as higher orders of temporal correlations are taken into account. We
then go on to show that, in addition to reducing complexity, anesthesia also
modulates the informational structure between the forward- and reverse-time
neural signals. Specifically, using three distinct notions of temporal
asymmetry we show that anesthesia reduces temporal asymmetry on
information-theoretic and information-geometric grounds. In contrast to prior
work, our results show that: (1) Complexity differences can emerge at very
short timescales and across broad regions of the fly brain, thus heralding the
macroscopic state of anesthesia in a previously unforeseen manner, and (2) that
general anesthesia also modulates the temporal asymmetry of neural signals.
Together, our results demonstrate that anesthetized brains become both less
structured and more reversible.Comment: 14 pages, 6 figures. Comments welcome; Added time-reversal analysis,
updated discussion, new figures (Fig. 5 & Fig. 6) and Tables (Tab. 1
Heuristic Ranking in Tightly Coupled Probabilistic Description Logics
The Semantic Web effort has steadily been gaining traction in the recent
years. In particular,Web search companies are recently realizing that their
products need to evolve towards having richer semantic search capabilities.
Description logics (DLs) have been adopted as the formal underpinnings for
Semantic Web languages used in describing ontologies. Reasoning under
uncertainty has recently taken a leading role in this arena, given the nature
of data found on theWeb. In this paper, we present a probabilistic extension of
the DL EL++ (which underlies the OWL2 EL profile) using Markov logic networks
(MLNs) as probabilistic semantics. This extension is tightly coupled, meaning
that probabilistic annotations in formulas can refer to objects in the
ontology. We show that, even though the tightly coupled nature of our language
means that many basic operations are data-intractable, we can leverage a
sublanguage of MLNs that allows to rank the atomic consequences of an ontology
relative to their probability values (called ranking queries) even when these
values are not fully computed. We present an anytime algorithm to answer
ranking queries, and provide an upper bound on the error that it incurs, as
well as a criterion to decide when results are guaranteed to be correct.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty
in Artificial Intelligence (UAI2012
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