Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/8578
Title: A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis
Authors: Chatzis, Sotirios P. 
Kosmopoulos, Dimitrios 
Keywords: Bayes methods
Convolution
Feature extraction
Image recognition
Independent component analysis
Issue Date: Dec-2015
Publisher: IEEE
Source: Proc. International Conference on Computer Vision (ICCV), 2015, Santiago, pages 2803 - 2811
Abstract: Unsupervised feature learning algorithms based on convolutional formulations of independent components analysis (ICA) have been demonstrated to yield state-of-the-art results in several action recognition benchmarks. However, existing approaches do not allow for the number of latent components (features) to be automatically inferred from the data in an unsupervised manner. This is a significant disadvantage of the state-of-the-art, as it results in considerable burden imposed on researchers and practitioners, who must resort to tedious cross-validation procedures to obtain the optimal number of latent features. To resolve these issues, in this paper we introduce a convolutional nonparametric Bayesian sparse ICA architecture for overcomplete feature learning from high-dimensional data. Our method utilizes an Indian buffet process prior to facilitate inference of the appropriate number of latent features under a hybrid variational inference algorithm, scalable to massive datasets. As we show, our model can be naturally used to obtain deep unsupervised hierarchical feature extractors, by greedily stacking successive model layers, similar to existing approaches. In addition, inference for this model is completely heuristics-free, thus, it obviates the need of tedious parameter tuning, which is a major challenge most deep learning approaches are faced with. We evaluate our method on several action recognition benchmarks, and exhibit its advantages over the state-of-the-art.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/8578
DOI: 10.1109/ICCV.2015.321
Rights: Copyright IEEE
Appears in Collections:Δημοσιεύσεις σε συνέδρια/Conference papers

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