This paper describes results from an ongoing project concerned with recognizing objects in complex scene domains, and especially in the domain that includes the natural outdoor world. Traditional machine recognition paradigms assume either (1) that all objects of interest are definable by a relatively small number of explicit shape models, or (2) that all objects of interest have characteristic, locally measurable features. The failure of both assumptions in a complex domain such as the natural outdoor world has a dramatic impact on the form of an acceptable architecture for an object recognition system. In our work, we make the use of contextual information a central issue, and explicitly design a system to identify and use context as an integral part of recognition. In so doing, we provide a new paradigm for visual recognition that eliminates the traditional dependence on stored geometric models and universal image partitioning algorithms. This paradigm combines the results of many s...

This paper addresses automatic interpretation of images of outdoor scenes. The method allows instances of objects from a number of generic classes to be identified: vegetation, buildings, vehicles, roads, etc., thereby enabling image databases to be queried on scene content. This is achieved

Retrieving images from very large collections using image content as a key is becoming an important problem. Classifying images into visual categories and finding objects in image databases are two major challenges in the field. This paper describes our approach toward the first of the two tasks, the generalization of which we believe will assist in the second task as well. We define a blobworld representation which provides a transition from the raw pixel data to a small set of localized coherent regions in color and texture space. Learning is then utilized to extract a probabilistic interpretation of the scene. Experimental results are presented for more than 1000 images from the Corel photo collection. 1. Introduction Very large collections of images are becoming common, and users have a clear preference for accessing images in these databases based on their content---be it the general image category (e.g., animal scenes, landscapes, urban scenes) or particular objects that are pre...

this paper we show how both these stages may be successfully solved using artificial neural networks. We use a self-organising feature map for the segmentation phase and a multi-layer perceptron for the region classification phase. The self-organising map is used to visually demonstrate the importance of using both texture and colour in a segmentation, while the multi-layer perceptron is used to achieve high classification performance. In Sec. 2 we demonstrate how a self-organising feature map (SOFM) may be used to automatically segment images. The quality of the segmentations is measured and the contribution made through use of colour and texture information is quantified. In Sec. 3 we show that features extracted from segmented regions, including size, shape, texture and

We introduce an automatic segmentation framework that blends the advantages of color, texture, shape, and motion based segmentation methods in a computationally feasible way. A spatiotemporal data structure is first constructed for each group of video frames, in which each pixel is assigned a feature vector based on lowlevel visual information. Then, the smallest homogeneous components, so called as volumes, are expanded from selected marker points using an adaptive, three dimensional, centroid-linkage method. Self descriptors that characterize each volume, and relational descriptors that capture the mutual properties between pairs of volumes are determined by evaluating the boundary, trajectory, and motion of the volumes. These descriptors are used to measure the similarity between volumes based on which volumes are further grouped into objects. A fine-to-coarse clustering algorithm yields a multi-resolution object tree representation as an output of the segmentation.

High-level vision systems use object, scene or domain specific knowledge to interpret images. Unfortunately, this knowledge has to be acquired for every domain. This makes it difficult to port systems from one domain to another, and therefore to compare them. Recently, the authors of the ADORE system have claimed that object recognition can be modeled as a Markov decision process, and that domain-specific control strategies can be inferred automatically from training data. In this paper we demonstrate the generality of this approach by porting ADORE to a new domain, where it controls an object recognition system that previously relied on a semantic network.

This paper discusses the problems of knowledge representa- tion in a distributed AI environment, and the Schema System's approach to those problems. A series of interpretation experiments have been performed on nine ages from two natural scene domalnsl results from three images will be presented here. Interested readers are referred to [6] for a full description of the system and the experiments

this paper we describe a system to show some limited effects on a static toy-car model and present techniques that can be used in similar setups. Our focus is on creating apparent motion for animation

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