There is general consensus that context can be a rich source of information about an object's identity, location and scale. In fact, the structure of many real-world scenes is governed by strong configurational rules akin to those that apply to a single object. Here we introduce a simple framework for modeling the relationship between context and object properties based on the correlation between the statistics of low-level features across the entire scene and the objects that it contains. The resulting scheme serves as an effective procedure for object priming, context driven focus of attention and automatic scale-selection on real-world scenes.

this article. This paper has also benefited greatly from constructive feedback from Gordon Logan, Mike Stadler, and our other reviewers. We thank Joanie Sanchez for her assistance in running Experiment 1. This research was supported by a Social Science Faculty Research Award from Yale University. Portions of this research were presented at the Annual Meeting of the Association for Research in Ophthalmology and Vision, Fort Lauderdale, FL, in May, 1997, and at the Annual Meeting of the Psychonomic Society, Philadelphia, PA, in November, 1997

We propose a computational model for detecting and localizing instances from an object class in static grey level images. We divide detection into visual selection and final classification, concentrating on the former: Drastically reducing the number of candidate regions which require further, usually more intensive, processing, but with a minimum of computation and missed detections. Bottom-up processing is based on local groupings of edge fragments constrained by loose geometrical relationships. They have no a priori semantic or geometric interpretation. The role of training is to select special groupings which are moderately likely at certain places on the object but rare in the background. We show that the statistics in both populations are stable. The candidate regions are those which contain global arrangements of several local groupings. Whereas our model was not conceived to explain brain functions, it does cohere with evidence about the functions of neurons in V1 and V2, such ...

Many experiments have shown that the human visual system makes extensive use of contextual information for facilitating object search in natural scenes. However, the question of how to formally model contextual influences is still open. On the basis of a Bayesian framework, the authors present an original approach of attentional guidance by global scene context. The model comprises 2 parallel pathways; one pathway computes local features (saliency) and the other computes global (scenecentered) features. The contextual guidance model of attention combines bottom-up saliency, scene context, and top-down mechanisms at an early stage of visual processing and predicts the image regions likely to be fixated by human observers performing natural search tasks in real-world scenes.

Many researchers interested in connectionist models accept that such models are "neurally inspired " but do not worry too much about whether their models are biologically realistic. While such a position may be perfectly justifiable, the present paper attempts to illustrate how biological information can be used to constrain connectionist models. Two particular areas are discussed. The first section deals with visual information processing in the primate and human visual system. It is argued that speed with which visual information is processed imposes major constraints on the architecture and operation of the visual system. In particular, it seems that a great deal of processing must depend on a single bottum-up pass. The second section deals with biological aspects of learning algorithms. It is argued that although there is good evidence for certain coactivation related synaptic modification schemes, other learning mechanisms, including back-propagation, are not currently supported by experimental data.

this paper a statistical framework for incorporating contextual information in the search task is proposed

In recognizing objects and scenes, partial recognition of objects or their parts could be used to guide the recognition of other objects. Here, we investigated psychophysically the role of local features in the recognition of complete figures, and the influence of contextual information on the identification of ambiguous features. We used configurations of features that were placed in either proper or improper spatial relations, and measured response times and error rates in a recognition task. Two main results were obtained. First, proper spatial relations among the features of a scene decrease response times and error rates in the recognition of individual features. Second, the presence of features that have a unique interpretation in the scene disambiguates the identity of ambiguous features faster and with less errors compared with the same features that appear in isolation, or in improper spatial relations. The implications of these findings to the organization of recognition memo...

Previous research has shown that background knowledge affects the ease of concept learning, but little research has examined its effects on speeded categorization of instances after the category is well learned. Subjects in 4 experiments first learned novel categories. At test, they categorized a new set of novel stimuli that were either consistent or inconsistent with background knowledge given about the categories. Background knowledge affected catego-rization responses in an untimed task, with usual reaction time instructions, with a response deadline, or when the stimuli were presented for 50 ms followed by a mask. Three other experiments using a part-detection task showed that subjects were more likely to notice missing parts that were critical than noncritical according to background knowledge. The mechanisms by which background knowledge affects categorization and part detection are discussed. Human categorization is a cognitive proceSs in which people decide whether an instance is a member of a cate-gory by comparing the instance with their conceptual rep-resentations. Categorization research in the 1970s and early

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Recent work in the computational modeling of visual attention has demonstrated that a purely bottom-up approach to identifying salient regions within an image can be successfully applied to diverse and practical problems from target recognition to the placement of advertisement. This paper proposes an application of a combination of computational models of visual attention to the image retrieval problem. We demonstrate that certain shortcomings of existing content-based image retrieval solutions can be addressed by implementing a biologically-motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual contents of other (less relevant) parts of the image. We propose a model in which only the salient regions of an image are encoded as ROIs whose features are then compared against previously seen ROIs and assigned cluster membership accordingly. Experimental results show that the proposed approach works well for several combinations of feature extraction techniques and clustering algorithms, suggesting a promising avenue for future improvements, such as the addition of a top-down component and the inclusion of a relevance feedback mechanism.