61 research outputs found
An integrated model of top-down and bottom-up attention for optimal object detection
No full textIntegration of goal-driven, top-down attention and image-driven, bottom-up attention is crucial for visual search. Yet, previous research has mostly focused on models that are purely top-down or bottom-up. Here, we propose a new model that combines both. The bottom-up component computes the visual salience of scene locations in different feature maps extracted at multiple spatial scales. The topdown component uses accumulated statistical knowledge of the visual features of the desired search target and background clutter, to optimally tune the bottom-up maps such that target detection speed is maximized. Testing on 750 artificial and natural scenes shows that the model’s predictions are consistent with a large body of available literature on human psychophysics of visual search. These results suggest that our model may provide good approximation of how humans combine bottom-up and top-down cues such as to optimize target detection speed. 1
A mathematical framework for the design and analysis of feature biasing strategies
No full textPlease see attached pdf file
Abstract
No full textSurvival in the natural world demands the selection of relevant visual cues to rapidly and reliably guide attention towards prey and predators in cluttered environments. We investigate whether our visual system selects cues that guide search in an optimal manner. We formally obtain the optimal cue selection strategy by maximizing the signal to noise ratio (SN R) between a search target and surrounding distractors. This optimal strategy successfully accounts for several phenomena in visual search behavior, including the effect of target-distractor discriminability, uncertainty in target’s features, distractor heterogeneity, and linear separability. Furthermore, the theory generates a new prediction, which we verify through psychophysical experiments with human subjects. Our results provide direct experimental evidence that humans select visual cues so as to maximize SNR between the targets and surrounding clutter.
A goal oriented attention guidance model
No full textAbstract. Previous experiments have shown that human attention is influenced by high level task demands. In this paper, we propose an architecture to estimate the task-relevance of attended locations in a scene. We maintain a task graph and compute relevance of fixations using an ontology that contains a description of real world entities and their relationships. Our model guides attention according to a topographic attention guidance map that encodes the bottom-up salience and task-relevance of all locations in the scene. We have demonstrated that our model detects entities that are salient and relevant to the task even on natural cluttered scenes and arbitrary tasks.
Search Goal Tunes Visual Features Optimally
No full textSummaryHow does a visual search goal modulate the activity of neurons encoding different visual features (e.g., color, direction of motion)? Previous research suggests that goal-driven attention enhances the gain of neurons representing the target's visual features. Here, we present mathematical and behavioral evidence that this strategy is suboptimal and that humans do not deploy it. We formally derive the optimal feature gain modulation theory, which combines information from both the target and distracting clutter to maximize the relative salience of the target. We qualitatively validate the theory against existing electrophysiological and psychophysical literature. A surprising prediction is that it is sometimes optimal to enhance nontarget features. We provide experimental evidence toward this through psychophysics experiments on human subjects, thus suggesting that humans deploy the optimal gain modulation strategy
Neuron Article Search Goal Tunes Visual Features Optimally
No full textHow does a visual search goal modulate the activity of neurons encoding different visual features (e.g., color, direction of motion)? Previous research suggests that goal-driven attention enhances the gain of neurons representing the target’s visual features. Here, we present mathematical and behavioral evidence that this strategy is suboptimal and that humans do not deploy it. We formally derive the optimal feature gain modulation theory, which combines information from both the target and distracting clutter to maximize the relative salience of the target. We qualitatively validate the theory against existing electrophysiological and psychophysical literature. A surprising prediction is that it is sometimes optimal to enhance nontarget features. We provide experimental evidence toward this through psychophysics experiments on human subjects, thus suggesting that humans deploy the optimal gain modulation strategy
Combining bottom-up and top-down attentional influences
No full textVisual attention to salient and relevant scene regions is crucial for an animal’s survival in the natural world. It is guided by a complex interplay of at least two factors – image-driven, bottom-up salience [1] and knowledge-driven, top-down guidance [2, 3]. For instance, a ripe red fruit among green leaves captures visual attention due to its bottom-up salience, while a non-salient camouflaged predator is detected through top-down guidance to known predator locations and features. Although both bottom-up and top-down factors are important for guiding visual attention, most existing models and theories are either purely top-down [4] or bottom-up [5, 6]. Here, we present a combined model of bottom-up and top-down visual attention. Our proposed model first computes the naive, bottom-up salience of every scene location for different local visual features (e.g., different colors, orientations and intensities) at mul-tiple spatial scales in a manner described in [6]. Next, the top-down component uses learnt statistical knowledge of the local features of the target and distracting clutter, to optimize the relative weights of the bottom-up maps such that the overall salience of the target is maximized relative to the surrounding clutter. Such optimization renders the target mor
Abstract Modeling the influence of task on attention
No full textWe propose a computational model for the task-specific guidance of visual attention in real-world scenes. Our model emphasizes four aspects that are important in biological vision: determining task-relevance of an entity, biasing attention for the low-level visual features of desired targets, recognizing these targets using the same low-level features, and incrementally building a visual map of task-relevance at every scene location. Given a task definition in the form of keywords, the model first determines and stores the task-relevant entities in working memory, using prior knowledge stored in long-term memory. It attempts to detect the most relevant entity by biasing its visual attention system with the entityÕs learned low-level features. It attends to the most salient location in the scene, and attempts to recognize the attended object through hierarchical matching against object representations stored in longterm memory. It updates its working memory with the task-relevance of the recognized entity and updates a topographic taskrelevance map with the location and relevance of the recognized entity. The model is tested on three types of tasks: single-target detection in 343 natural and synthetic images, where biasing for the target accelerates target detection over twofold on average; sequential multiple-target detection in 28 natural images, where biasing, recognition, working memory and long term memory contribute to rapidly finding all targets; and learning a map of likely locations of cars from a video clip filmed while driving on a highway. The modelÕs performance on search for single features and feature conjunctions is consistent with existing psychophysical data. These results of our biologically-motivated architecture suggest that the model may provide a reasonable approximation t
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