Image Classification via Adaptive Ensemblesof Descriptor-Specific Classifiers

摘要

An automated classification system usually consists of (i) a supervised learning algorithm for auto-matically generating classifiers from training data, and (ii) a representation scheme for converting the trainingobjects into vectorial representations of their content. In this work, we take a detour from this tradition andpresent an approach to image classification based on an adaptive ensemble of classifiers, each specialized onclassifying images based on a single "descriptor." Each descriptor focuses on a different aspect, or perspective,of images; an ensemble of descriptor-specific classifiers can thus be seen as a committee of experts, each viewingthe problem to be solved with a different slant, of from a different viewpoint. We test four different ways to set upsuch an ensemble, based on different ways of leveraging on the individual responses returned by each memberof the ensemble, and on how confident these members are on their responses. We test this approach by using fivedifferent M PEG-7 descriptors on the task of assigning photographs of stone slabs to classes representing differ-ent types of stones. Our experimental results show important accuracy improvements with respect to a baselinein which a single classifier, working an all five descriptors at the same time, is employed.