Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

Gutmann, Michael U.; Laparra, Valero; Hyvärinen, Aapo; Malo, Jesús

doi:10.1371/journal.pone.0086481

Cited by 23 publications

(37 citation statements)

References 52 publications

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“…In the image processing side, the relevance of the inverse is obvious in perceptual image/video coding where the signal is transformed to the perceptual representation prior to quantization [13,[35][36][37]: decompression implies the inverse to reconstruct the image. Another example is white balance based on human color constancy (or chromatic adaptation): in general, adaptation may be understood as a transform to an invariant representation which is insensitive to irrelevant changes (as for instance the nature of the illumination) [46][47][48]. Models of this class of invariant representations could be easily applied for color constancy if the transform is invertible.…”

Section: Introductionmentioning

confidence: 99%

Derivatives and inverse of cascaded linear+nonlinear neural models

et al. 2018

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In vision science, cascades of Linear+Nonlinear transforms are very successful in modeling a number of perceptual experiences. However, the conventional literature is usually too focused on only describing the forward input-output transform. Instead, in this work we present the mathematics of such cascades beyond the forward transform, namely the Jacobian matrices and the inverse. The fundamental reason for this analytical treatment is that it offers useful analytical insight into the psychophysics, the physiology, and the function of the visual system. For instance, we show how the trends of the sensitivity (volume of the discrimination regions) and the adaptation of the receptive fields can be identified in the expression of the Jacobian w.r.t. the stimulus. This matrix also tells us which regions of the stimulus space are encoded more efficiently in multi-information terms. The Jacobian w.r.t. the parameters shows which aspects of the model have bigger impact in the response, and hence their relative relevance. The analytic inverse implies conditions for the response and model parameters to ensure appropriate decoding. From the experimental and applied perspective, (a) the Jacobian w.r.t. the stimulus is necessary in new experimental methods based on the synthesis of visual stimuli with interesting geometrical properties, (b) the Jacobian matrices w.r.t. the parameters are convenient to learn the model from classical experiments or alternative goal optimization, and (c) the inverse is a promising model-based alternative to blind machine-learning methods for neural decoding that do not include meaningful biological information. The theory is checked by building and testing a vision model that actually follows a modular Linear+Nonlinear program. Our illustrative derivable and invertible model consists of a cascade of modules that account for brightness, contrast, energy masking, and wavelet masking. To stress the generality of this modular setting we show examples where some of the canonical Divisive Normalization modules are substituted by equivalent modules such as the Wilson-Cowan interaction model (at the V1 cortex) or a tone-mapping model (at the retina).

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Section: Introductionmentioning

confidence: 99%

Derivatives and inverse of cascaded linear+nonlinear neural models

et al. 2018

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“…Examples of statistical links between changes in the signal and the perceptual reference system include spatio-chromatic receptive fields (Gutmann et al, 2014 ), and purely chromatic sensors (Laparra et al, 2012 ) in different illuminations. In the same way, unsupervised learning with well-defined goals should be used to explain aftereffects in scenes with unusual motion, contrast, illumination, or reflectance distribution.…”

Section: Introductionmentioning

confidence: 99%

“…This is the normative approach we propose in this work: we show that an appropriate unsupervised learning tool, the Sequential Principal Curves Analysis (SPCA) (Laparra et al, 2012 ), captures the statistical trends in natural movies, textures, and colors, which explain the aftereffects. As opposed to previous normative approaches (Barlow, 1990 ; Webster and Mollon, 1997 ; Wainwright, 1999 ; Gutmann et al, 2014 ), that use linear techniques to maximize independence and match the manifolds in different environments, SPCA is a more flexible, not necessarily linear, equalization. As a result, it can also account for the experimental saturation found in the motion, texture, and color sensors.…”

Section: Introductionmentioning

confidence: 99%

Visual aftereffects and sensory nonlinearities from a single statistical framework

Laparra

Malo

2015

Front. Hum. Neurosci.

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When adapted to a particular scenery our senses may fool us: colors are misinterpreted, certain spatial patterns seem to fade out, and static objects appear to move in reverse. A mere empirical description of the mechanisms tuned to color, texture, and motion may tell us where these visual illusions come from. However, such empirical models of gain control do not explain why these mechanisms work in this apparently dysfunctional manner. Current normative explanations of aftereffects based on scene statistics derive gain changes by (1) invoking decorrelation and linear manifold matching/equalization, or (2) using nonlinear divisive normalization obtained from parametric scene models. These principled approaches have different drawbacks: the first is not compatible with the known saturation nonlinearities in the sensors and it cannot fully accomplish information maximization due to its linear nature. In the second, gain change is almost determined a priori by the assumed parametric image model linked to divisive normalization. In this study we show that both the response changes that lead to aftereffects and the nonlinear behavior can be simultaneously derived from a single statistical framework: the Sequential Principal Curves Analysis (SPCA). As opposed to mechanistic models, SPCA is not intended to describe how physiological sensors work, but it is focused on explaining why they behave as they do. Nonparametric SPCA has two key advantages as a normative model of adaptation: (i) it is better than linear techniques as it is a flexible equalization that can be tuned for more sensible criteria other than plain decorrelation (either full information maximization or error minimization); and (ii) it makes no a priori functional assumption regarding the nonlinearity, so the saturations emerge directly from the scene data and the goal (and not from the assumed function). It turns out that the optimal responses derived from these more sensible criteria and SPCA are consistent with dysfunctional behaviors such as aftereffects.

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“…In the solution, the global optimal solution cannot be generalized, and only the objective function cannot be obtained that can be drawn noninferior solution set, the collection of elements are not dominant. To solve optimization problem, we use the prior discussed methodology to achieve the goal [12][13][14][15].…”

Section: Environmental Quality Modellingmentioning

confidence: 99%

Construction and Simulation on Environmental Quality Evaluation Model Based on Data Mining and Correlation Analysis

Wang¹,

Duoyong²,

Xu³

2017

IJDTA

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In this paper, we conduct research on environmental quality evaluation model based on data mining and correlation analysis. Along with the application of multi-statistical analysis method, the big data analysis law by has been applied in the environmental quality evaluation. Reciprocities of this method among from many targets starts that changes into a few not related overall targets many targets and the merit lies in had considered the relevance among various targets that can maximum limit retain original information, carries on best comprehensive dimensionality reduction processing to the high dimensional data. Aside by using this feature, this paper proposes the data mining and correlation analysis based model. The basic task of the analytical grey incidence is the microscopic or macroscopic geometry of behavior based factor sequence is close, toanalyze and contribution degree of influence or the factor between determination factors to main behavior, but the gray incidence space carries on the foundation of analytical grey incidence. We implement the model on the air and water quality evaluation which are assisted with the neural network and gray analysis. The experimental result reflect the effectiveness of our model, it can evaluate the environmental quality effectively.

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Spatio-Chromatic Adaptation via Higher-Order Canonical Correlation Analysis of Natural Images

Cited by 23 publications

References 52 publications

Derivatives and inverse of cascaded linear+nonlinear neural models

Derivatives and inverse of cascaded linear+nonlinear neural models

Visual aftereffects and sensory nonlinearities from a single statistical framework

Construction and Simulation on Environmental Quality Evaluation Model Based on Data Mining and Correlation Analysis

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