Chromatic Information and Feature Detection in Fast Visual Analysis

Fast discrimination of fragmentary images: the role of local optimal information

In naturalistic conditions, objects in the scene may be partly occluded and the visual system has to recognize the whole image based on the little information contained in some visible fragments. Previous studies demonstrated that humans can successfully recognize severely occluded images, but the underlying mechanisms occurring in the early stages of visual processing are still poorly understood. The main objective of this work is to investigate the contribution of local information contained in a few visible fragments to image discrimination in fast vision. It has been already shown that a specific set of features, predicted by a constrained maximum-entropy model to be optimal carriers of information (optimal features), are used to build simplified early visual representations (primal sketch) that are sufficient for fast image discrimination. These features are also considered salient by the visual system and can guide visual attention when presented isolated in artificial stimuli. Here, we explore whether these local features also play a significant role in more natural settings, where all existing features are kept, but the overall available information is drastically reduced. Indeed, the task requires discrimination of naturalistic images based on a very brief presentation (25 ms) of a few small visible image fragments. In the main experiment, we reduced the possibility to perform the task based on global-luminance positional cues by presenting randomly inverted-contrast images, and we measured how much observers' performance relies on the local features contained in the fragments or on global information. The size and the number of fragments were determined in two preliminary experiments. Results show that observers are very skilled in fast image discrimination, even when a drastic occlusion is applied. When observers cannot rely on the position of global-luminance information, the probability of correct discrimination increases when the visible fragments contain a high number of optimal features. These results suggest that such optimal local information contributes to the successful reconstruction of naturalistic images even in challenging conditions.

Information-optimal local features automatically attract covert and overt attention

In fast vision, local spatial properties of the visual scene can automatically capture the observer's attention. We used specific local features, predicted by a constrained maximum-entropy model to be optimal information-carriers, as candidate "salient features''. Previous studies showed that participants choose these optimal features as "more salient" if explicitly asked. Here, we investigated the implicit saliency of these optimal features in two attentional tasks. In a covert-attention experiment, we measured the luminance-contrast threshold for discriminating the orientation of a peripheral gabor. In a gaze-orienting experiment, we analyzed latency and direction of saccades towards a peripheral target. In both tasks, two brief peripheral cues, differing in saliency according to the model, preceded the target, presented on the same (valid trials) or the opposite side (invalid trials) of the optimal cue. Results showed reduced contrast thresholds, saccadic latencies, and direction errors in valid trials, and the opposite in invalid trials, compared to baseline values obtained with equally salient cues. Also, optimal features triggered more anticipatory saccades. Similar effects emerged in a luminance-control condition. Overall, in fast vision, optimal features automatically attract covert and overt attention, suggesting that saliency is determined by information maximization criteria coupled with computational limitations.

Early Visual Saliency Based on Isolated Optimal Features

Under fast viewing conditions, the visual system extracts salient and simplified representations of complex visual scenes. Saccadic eye movements optimize such visual analysis through the dynamic sampling of the most informative and salient regions in the scene. However, a general definition of saliency, as well as its role for natural active vision, is still a matter for discussion. Following the general idea that visual saliency may be based on the amount of local information, a recent constrained maximum-entropy model of early vision, applied to natural images, extracts a set of local optimal information-carriers, as candidate salient features. These optimal features proved to be more informative than others in fast vision, when embedded in simplified sketches of natural images. In the present study, for the first time, these features were presented in isolation, to investigate whether they can be visually more salient than other non-optimal features, even in the absence of any meaningful global arrangement (contour, line, etc.). In four psychophysics experiments, fast discriminability of a compound of optimal features (target) in comparison with a similar compound of non-optimal features (distractor) was measured as a function of their number and contrast. Results showed that the saliency predictions from the constrained maximum-entropy model are well verified in the data, even when the optimal features are presented in smaller numbers or at lower contrast. In the eye movements experiment, the target and the distractor compounds were presented in the periphery at different angles. Participants were asked to perform a simple choice-saccade task. Results showed that saccades can select informative optimal features spatially interleaved with non-optimal features even at the shortest latencies. Saccades’ choice accuracy and landing position precision improved with SNR. In conclusion, the optimal features predicted by the reference model, turn out to be more salient than others, despite the lack of any clues coming from a global meaningful structure, suggesting that they get preferential treatment during fast image analysis. Also, peripheral fast visual processing of these informative local features is able to guide gaze orientation. We speculate that active vision is efficiently adapted to maximize information in natural visual scenes.