Image classification and object detection using spatial contextual constraints

Abstract

Spatial information plays a very important role in high-level image understanding tasks. Contextual models that exploit spatial information through the quantification of region spatial relationships can be used for resolving the uncertainties in low-level features used for image classification and object detection. We describe intuitive, flexible and efficient methods for modeling pairwise directional spatial relationships and the ternary between relationship using fuzzy mathematical morphology. These methods define a fuzzy landscape where each image point is assigned a value that quantifies its relative position with respect to the reference object(s) and the type of the relationship. Directional mathematical dilation with fuzzy structuring elements is used to compute this landscape. We provide flexible definitions of fuzzy structuring elements that are tunable along both radial and angular dimensions. Examples using synthetic images show that our models produce more intuitive results than the competitors. We also illustrate the use of the models described in this chapter as spatial contextual constraints for two image analysis tasks. First, we show how these spatial relationships can be incorporated into a Bayesian classification framework for land cover classification to reduce the amount of commission among spectrally similar classes. Then, we show how the use of spatial constraints derived from shadow regions improves building detection accuracy. The significant improvement in accuracy in these applications confirms the importance of spatial information and the effectiveness of the relationship models described in this chapter in modeling and quantifying this information.