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This paper presents an unsupervized offline video object segmentation method based on features combination.
In order to better manage features combination at runtime, it separates the variability in domain and code representation spaces.
Subsequently, this established multidimensional feature space is compressed to a two-dimension plane by the optimal features combination technique, and UWB signal detection is consequently formulated to assign these pattern points into two classes at the minimum errors criterion.
The core step in our feature selection process is the application of genetic algorithm (GA) in search of an optimal features combination.
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A quantitative study of selected features combinations is then provided.
Several feature combination rules were tested by combining the similarity maps using different computations.
Figure 21 Flowchart of feature combination.
Figure 2 DTW-based feature combination of two sample utterances.
Figure 2 sketches the feature combination of two samples.
From the results it has been observed that MPCA + QTLR feature combination outperforms PCA + QTLR feature combination and most other conventional features available in the literature.
In both experiments, we use SIFT feature combination with the LNBNN classifier.
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