Múltiplos fluxos ópticos e o processamento atentivo da informação visual

The environment in which we live in, we constantly deal with a huge amount of dynamic information, thus, attention is an indispensable cognitive resource that allows an effective selection of stimuli for our survival. From this, investigating how we process our encouragement in movements and how the attention spreads into a space to serve more than one stimuli simultanously is something very important. The behavioural urgence hipothesis suggests that the encouragement in a movement of approaching shows a certain priority in the process related to objects which are in a movement away, but there are researches that point out that it might not happen in an attentive phase, but instead as a priorization of motor response. There are also many controversies found in researches about attentive focalization, in which some studies suggest that the focus of attention would work in a similar manner to a zoom lens, while some searches indicate that the focus of attention could be shared to answer some stimuli in non contiguous regions. This study tried to investigate through two experiments the effect of attentive priorization by encouragement in movements and how the attention is spread with distractors stimuli. The first experiment investigated if the amount of moving flows really influenced in the process of information. The results indicate an effect of priorization of the flows guided in relation to aleatory ones and also from the unique flow due to dual flow. The second experiment investigated how the distribution of attention is in a space with the use of flows as an exogenous cue. The results indicate that the focus of attention works as the one suggested in the zoom lens model.

O uso da Divergência de Kullback-Leibler e da Divergência Generalizada como medida de similaridade em sistemas CBIR

The content-based image retrieval is important for various purposes like disease diagnoses from computerized tomography, for example. The relevance, social and economic of image retrieval systems has created the necessity of its improvement. Within this context, the content-based image retrieval systems are composed of two stages, the feature extraction and similarity measurement. The stage of similarity is still a challenge due to the wide variety of similarity measurement functions, which can be combined with the different techniques present in the recovery process and return results that aren’t always the most satisfactory. The most common functions used to measure the similarity are the Euclidean and Cosine, but some researchers have noted some limitations in these functions conventional proximity, in the step of search by similarity. For that reason, the Bregman divergences (Kullback Leibler and I-Generalized) have attracted the attention of researchers, due to its flexibility in the similarity analysis. Thus, the aim of this research was to conduct a comparative study over the use of Bregman divergences in relation the Euclidean and Cosine functions, in the step similarity of content-based image retrieval, checking the advantages and disadvantages of each function. For this, it was created a content-based image retrieval system in two stages: offline and online, using approaches BSM, FISM, BoVW and BoVW-SPM. With this system was created three groups of experiments using databases: Caltech101, Oxford and UK-bench. The performance of content-based image retrieval system using the different functions of similarity was tested through of evaluation measures: Mean Average Precision, normalized Discounted Cumulative Gain, precision at k, precision x recall. Finally, this study shows that the use of Bregman divergences (Kullback Leibler and Generalized) obtains better results than the Euclidean and Cosine measures with significant gains for content-based image retrieval.