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In the case of shape contexts, we subsampled the contour down to 50 points and then obtained the shape contexts features for this subset, obtaining a vector of 300 components.
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Table 3 compares the performance with and without the shape context features.
Table 2 shows the best classification result for the skating dataset with shape context features.
Table 4 shows the best performance achieved with shape context features.
In today's Smartphone eco-systems, such context features can be obtained easily.
The average accuracy for three activities is 81.5% with or without the shape context features.
A complete list of context features are shown in Table 1.
The proposed approach applies a rich feature set, including linguistic, orthographic, morphological, dictionary matching and local context features.
Firstly, we propose a context measuring mechanism which could explicitly assess roles of context features for different object recognition tasks.
Category std sit cam stand-spin 0.83 0 0.17 sit-spin 0 1 0 camel-spin 0.33 0 0.67 Table 3 Performance comparison between different methods from top to bottom: pLSA, without shape context features (SCF), and with shape context features (SCFs).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com