Quantifying and Transferring Contextual Information in Object Detection

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Nonstimulated early visual areas carry information about surrounding context

Even within the early sensory areas, the majority of the input to any given cortical neuron comes from other cortical neurons. To extend our knowledge of the contextual information that is transmitted by such lateral and feedback connections, we investigated how visually nonstimulated regions in primary visual cortex (V1) and visual area V2 are influenced by the surrounding context. We used functional magnetic resonance imaging (fMRI) and pattern-classification methods to show that the cortical representation of a nonstimulated quarter-field carries information that can discriminate the surrounding visual context. We show further that the activity patterns in these regions are significantly related to those observed with feed-forward stimulation and that these effects are driven primarily by V1. These results thus demonstrate that visual context strongly influences early visual areas even in the absence of differential feed-forward thalamic stimulation

Predictive User Modeling with Actionable Attributes

Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches

Exemplar-based Linear Discriminant Analysis for Robust Object Tracking

Tracking-by-detection has become an attractive tracking technique, which treats tracking as a category detection problem. However, the task in tracking is to search for a specific object, rather than an object category as in detection. In this paper, we propose a novel tracking framework based on exemplar detector rather than category detector. The proposed tracker is an ensemble of exemplar-based linear discriminant analysis (ELDA) detectors. Each detector is quite specific and discriminative, because it is trained by a single object instance and massive negatives. To improve its adaptivity, we update both object and background models. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our tracking algorithm.Comment: ICIP201

The perception of English front vowels by North Holland and Flemish listeners: acoustic similarity predicts and explains cross-linguistic and L2 perception

We investigated whether regional differences in the native language (L1) influence the perception of second language (L2) sounds. Many cross-language and L2 perception studies have assumed that the degree of acoustic similarity between L1 and L2 sounds predicts cross-linguistic and L2 performance. The present study tests this assumption by examining the perception of the English contrast between /e{open}/ and /æ/ in native speakers of Dutch spoken in North Holland (the Netherlands) and in East- and West-Flanders (Belgium). A Linear Discriminant Analysis on acoustic data from both dialects showed that their differences in vowel production, as reported in and Adank, van Hout, and Van de Velde (2007), should influence the perception of the L2 vowels if listeners focus on the vowels' acoustic/auditory properties. Indeed, the results of categorization tasks with Dutch or English vowels as response options showed that the two listener groups differed as predicted by the discriminant analysis. Moreover, the results of the English categorization task revealed that both groups of Dutch listeners displayed the asymmetric pattern found in previous word recognition studies, i.e. English /æ/ was more frequently confused with English /e{open}/ than the reverse. This suggests a strong link between previous L2 word learning results and the present L2 perceptual assimilation patterns