Comparative study of shape retrieval using feature fusion approaches

A thorough comparison of shape similarity distance measures for Content-Based Image Retrieval (CBIR) and application of feature normalization and machine learning has received limited attention. This article reports on the comparison of the performance of several shape similarity algorithms and the effect of several feature normalization methods. We also propose a learning-based feature selection and fusion scheme as an approach to bridge the `semantic gap´ between low-level image features and high-level human concepts. The methods are tested on a collection of segmented vertebral boundaries extracted from a subset of digitized x-ray images of the spine from the second National Health and Nutrition Examination Survey (NHANES II). In general the experimental results show that proper multi-feature fusion schemes achieve significantly improved retrieval performance.