We propose an efficient and robust method for the recognition of objects exhibiting multiple intra-class modes, where each one is associated with a particular object appearance. The proposed method, called random clustering ferns, combines synergically a single and real-time classifier, based on the boosted assembling of extremely randomized trees (ferns), with an unsupervised and probabilistic approach in order to recognize efficiently object instances in images and discover simultaneously the most prominent appearance modes of the object through tree-structured visual words. In particular, we use boosted random ferns and probabilistic latent semantic analysis to obtain a discriminative and multimodal classifier that automatically clusters the response of its randomized trees in function of the visual object appearance. The proposed method is validated extensively in synthetic and real experiments, showing that the method is capable of detecting objects with diverse and complex appearance distributions in real-time performance.


computer vision, object detection, pattern clustering.

Author keywords

recognition, random trees, pLSA, boosting

Scientific reference

M. Villamizar, A. Garrell Zulueta, A. Sanfeliu and F. Moreno-Noguer. Random clustering ferns for multimodal object recognition. Neural Computing and Applications, 28(9): 2445-2460, 2017.