Non-negative Matrix Factorization in Multimodality Data for Segmentation and Label Prediction

Zeynep Akata 1 Christian Thurau 2 Christian Bauckhage 2
1 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : With the increasing availability of annotated multimedia data on the Internet, techniques are in demand that allow for a principled joint processing of different types of data. Multiview learning and multiview clustering attempt to identify latent components in different features spaces in a simultaneous manner. The resulting basis vectors or centroids faithfully represent the different views on the data but are implicitly coupled and they were jointly estimated. This opens new avenues to problems such as label prediction, image retrieval, or semantic grouping. In this paper, we present a new model for multiview clustering that extends traditional non-negative matrix factorization to the joint factorization of different data matrices. Accordingly, the technique provides a new approach to the joint treatment of image parts and attributes. First experiments in image segmentation and multiview clustering of image features and image labels show promising results and indicate that the proposed method offers a common framework for image analysis on different levels of abstraction.
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Zeynep Akata, Christian Thurau, Christian Bauckhage. Non-negative Matrix Factorization in Multimodality Data for Segmentation and Label Prediction. 16th Computer Vision Winter Workshop, Feb 2011, Mitterberg, Austria. ⟨hal-00652879⟩

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