This paper presents a scheme of image retrieval from a database using queries prompted by the colour and the shape of the objects present in di erent scenes. Of the whole scheme of image retrieval, we will focus attention on the modules that allow feature extraction of the component objects from the scenes and the matching of the objects among the di erent images. The de®ned scheme enables the indexing of images by measuring the similarity between the integral objects. Ó
This paper evaluates different Restricted Boltzmann Machines models in unsupervised, semi-supervised and supervised frameworks using information from human actions. After feeding these multilayer models with low level features, we infer high-level discriminating features that highly improve the classification performance. This approach eliminates the difficult process of selecting good mid-level feature descriptors, changing the feature selection and extraction process by a learning stage. Two main contributions are presented. First, a new sequence-descriptor from accumulated histograms of optical flow (aHOF) is presented. Second, comparative results using unsupervised, supervised and semisupervised classification experiments are shown. The results show that the RBM architectures provide very good results in our classification task and present very good properties for semi-supervised learning.
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