An Iterative Combination Scheme for multimodal visual feature detection

Neurocomputing（2013）

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摘要

We address the problem of multimodal visual feature detection where several individual heterogeneous measures (i.e., feature detectors) are merged into a single saliency value. A newapproach, the Iterative Combination Scheme, is proposed to iteratively learn a classifier that infers a non-linear model to combine different feature detectors. We evaluate and compare the combination strategies presented using an objective methodology, the repeatability criterion, and a dataset with real images of 21 cluttered scenes of 3D objects.

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关键词

visual feature,learning (artificial intelligence),iterative combination scheme,saliency value,feature extraction,image classification,repeatability criterion,classifier learning,object detection,multimodal detection,multimodal visual feature detection,3d object,iterative methods,learning artificial intelligence

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