Spatial locality-aware sparse coding and dictionary learning
Nonlinear encoding of SIFT features has recently shown good promise in image classification. This scheme is able to reduce the training complexity of the traditional bag-of-feature approaches while achieving better performance. As a result, it is suitable for large-scale image classification applica...
محفوظ في:
| المؤلفون الرئيسيون: | , , , |
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| مؤلفون آخرون: | |
| التنسيق: | Conference or Workshop Item |
| اللغة: | English |
| منشور في: |
2014
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/106281 http://hdl.handle.net/10220/24002 http://jmlr.org/proceedings/papers/v25/wang12a/wang12a.pdf |
| الوسوم: |
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| الملخص: | Nonlinear encoding of SIFT features has recently shown good promise in image classification. This scheme is able to reduce the training complexity of the traditional bag-of-feature approaches while achieving better performance. As a result, it is suitable for large-scale image classification applications. However, existing nonlinear encoding methods do not explicitly consider the spatial relationship when encoding the local features, but merely leaving the spatial information used at a later stage, e.g. through the spatial pyramid matching, is largely inadequate. In this paper, we propose a joint sparse coding and dictionary learning scheme that take the spatial information into consideration in encoding. Our experiments on synthetic data and benchmark data demonstrate that the proposed scheme can learn a better dictionary and achieve higher classification accuracy. |
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