Color naming across languages reflects color use

What determines how languages categorize colors? We analyzed results of the World Color Survey (WCS) of 110 languages to show that despite gross differences across languages, communication of chromatic chips is always better for warm colors (yellows/reds) than cool colors (blues/greens). We present an analysis of color statistics in a large databank of natural images curated by human observers for salient objects and show that objects tend to have warm rather than cool colors. These results suggest that the cross-linguistic similarity in color-naming efficiency reflects colors of universal usefulness and provide an account of a principle (color use) that governs how color categories come about. We show that potential methodological issues with the WCS do not corrupt information-theoretic analyses, by collecting original data using two extreme versions of the color-naming task, in three groups: the Tsimane’, a remote Amazonian hunter-gatherer isolate; Bolivian-Spanish speakers; and English speakers. These data also enabled us to test another prediction of the color-usefulness hypothesis: that differences in color categorization between languages are caused by differences in overall usefulness of color to a culture. In support, we found that color naming among Tsimane’ had relatively low communicative efficiency, and the Tsimane’ were less likely to use color terms when describing familiar objects. Color-naming among Tsimane’ was boosted when naming artificially colored objects compared with natural objects, suggesting that industrialization promotes color usefulness.National Science Foundation (U.S.) (Award 1534318

Perceptual abstraction and attention

This is a report on the preliminary achievements of WP4 of the IM-CleVeR project on abstraction for cumulative learning, in particular directed to: (1) producing algorithms to develop abstraction features under top-down action influence; (2) algorithms for supporting detection of change in motion pictures; (3) developing attention and vergence control on the basis of locally computed rewards; (4) searching abstract representations suitable for the LCAS framework; (5) developing predictors based on information theory to support novelty detection. The report is organized around these 5 tasks that are part of WP4. We provide a synthetic description of the work done for each task by the partners

Novelty detection and learning drives

This document presents Deliverable 5.1 of the IM-CLeVeR (Intrinsically Motivated Cumulative Learning Versatile Robots) EU FP7 project. It represents one of two deliverables from Workpackage 5 (Novelty Detection and Drives for Autonomous Learning)

Pixel level colour constancy

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A spectral reflectance representation for recognition and reproduction

In this paper we present a method to recover a spectra representation for reproduction and recognition on multispectral imagery. To do this, we commence by viewing the spectra in the image as a mixture which can be expressed in terms of the sample mean and a set of basis vectors and weights. This treatment leads to an MAP approach where the sample means is given by the centers yielded by the application of the k-means clustering algorithm and the basis vectors are the eigenvectors of the corresponding covariance matrix. We compute the weights making use of a linear programming approach. We illustrate the utility of the method for purposes of skin recognition and spectra reconsruction

A Spiking Neural Network for Illuminant-invariant Colour Discrimination

Abstract — In this paper, we propose a biologically inspired spiking neural network approach to obtaining an opponent pair which is invariant to illumination variations and can be employed for colour discrimination. The model is motivated by the neural mechanisms involved in processing the visual stimulus starting from the cone photo receptors to the centresurround receptive fields present in the retinal ganglion cells and the striate cortex. For our spiking neural network, we have employed the excitatory and inhibitory lateral synaptic connections, the Spike-Timing Dependent Plasticity (STDP) and long term potentiation and depression (LTP/LTD). Here, we employ a feed-forward leaky integrate-and-fire spiking neural network trained using a dataset of Munsell spectra. We have performed tests on perceptually similar colours under large illuminant power variations and done experiments on colourbased object recognition. We have also compared our results to those yielded by a number of alternatives. I

Analysis of colour constancy algorithms using the knowledge of variation of correlated colour temperature of daylight with solar elevation

In this article, we present an investigation of possible improvement of the colour constant reflectance features that can be obtained from daylight illuminated scenes using pixel-level colour constancy algorithms (model-based algorithm: S Ratnasingam, S