Image Matching Using High Dynamic Range Images and Radial Feature Descriptors
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
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This paper addresses the challenge of identifying and retrieving related scenes from image databases with a focus on low-level feature descriptor construction. A set of affine covariant regions are identified via a radial segmentation algorithm. Local descriptors are built using two different types of histograms: (i) Polar Image Gradient (PIG) orientation histogram, and (ii) saturation-weighted hue histogram. The combination of geometric and photometric information yields a significant improvement in a feature's discriminative power. A cascading matching algorithm is used for feature matching and evaluation. To demonstrate the descriptor's image matching capabilities, a voting algorithm for similar scene retrieval is implemented utilizing results from the feature matches. Challenging images of buildings with inherent replicative feature regions due to common edificial texture are used to test the robustness and applicability of the radial-based methodology.