AdaBoost in region-based image retrieval
Shengyang Dai, Yu‐Jin Zhang · 2004
In this paper, a region-based AdaBoost (RBA) algorithm that combines the similarity contributions from different regions in images to form a single value for measuring similarity between images is proposed. The region-based framework utilizes the segmentation result to capture the higher-level concept of images. AdaBoost is a method of finding a highly accurate classifier by combining weak classifiers. A modified version of AdaBoost which can get confidence-rated prediction is applied to learn the final similarity function from user's feedback. It is based on a novel selection of weak classifiers. Experimental and comparison results, which are performed using a general-purpose database containing 7000 images, are promising.