Skip to Main Content
We propose an object boundary descriptor that facilitates the use-features on and off an object boundary for image retrieval. A string is used to model multiple continuous image and shape feature values on an object boundary. On the basis of these feature values and their higher-order derivatives the Taylor expansion provides an approximation of feature values in the immediate neighborhood of the object boundary. This object boundary description is employed within an existing population-based incremental interactive visual concept learning method for image retrieval. A set of 245 vertebral X-ray images is used to measure effects off the proposed descriptor in terms of number of relevance feedback steps and precision versus recall. Results show increased efficiency and efficacy.