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Feature Coding in Bags-of-Words| old_uid | 10654 |
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| title | Feature Coding in Bags-of-Words |
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| start_date | 2012/01/10 |
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| schedule | 15h-17h |
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| online | no |
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| location_info | salle 384 |
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| details | Séminaire MIDI |
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| summary | This presentation focuses on a number of steps in Bags-Of-Words including: i) segmentation-based descriptor design, ii) descriptor-to-visual-vocabulary coding step including Soft Assignment and its connection to Linear Coordinate Coding methods, and iii) Spatial Coordinate Coding to reduce histogram representations with Dominant Angle and Colour Pyramid Match to exploit non-spatial bias in images.
Regarding i), segmentation-based image descriptors for object category recognition were investigated. In contrast to commonly used interest points the proposed descriptors are extracted from pairs of adjacent regions given by a segmentation method. In this way we exploit semi-local structural information from the image.
Regarding ii), we show that one can take two views on Soft Assignment: an approach derived from Gaussian Mixture Model or special case of Linear Coordinate Coding. The latter view helped us propose how to optimise smoothing factor of Soft Assignment in a way that minimises descriptor reconstruction error and maximises classification performance.
Regarding iii), alternative ways of introducing spatial information during formation of histograms were investigated. Specifically, we proposed to apply spatial location information at a descriptor level (Spatial Coordinate Coding). Lastly, we demonstrated that Pyramid Match can be applied robustly to other measurements: Dominant Angle and Colour. |
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| responsibles | Marinica |
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