This paper presents a scene categorization method that is invariant to affine transformations. We propose a new moment-based normalization algorithm to generate an output image that is independent of the position, rot...
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ISBN:
(纸本)9781479957521
This paper presents a scene categorization method that is invariant to affine transformations. We propose a new moment-based normalization algorithm to generate an output image that is independent of the position, rotation, shear, and scale of the input image. In the proposed approach, an affine transform matrix is determined subject to the normalized image satisfying a set of moment constraints. After image normalization, a dense set of local features is extracted using scattering transform, and the global features are then formed via a sparse coding method. We evaluate the proposed method and other state-of-the-art algorithms on a benchmark dataset. The experimental results show that for images distorted with affine transformations, the proposed normalization increases the classification rate by about 28%, compared with the scene categorization approach that uses no normalization.
sparsecoding was shown to be able to find succinct representations of stimuli. Recently, it has been successfully applied to a variety of problems in image processing analysis. sparsecoding models data vectors as a ...
详细信息
ISBN:
(纸本)9781479957521
sparsecoding was shown to be able to find succinct representations of stimuli. Recently, it has been successfully applied to a variety of problems in image processing analysis. sparsecoding models data vectors as a linear combination of a few elements from a dictionary. However, most existing sparse coding methods are applied for a single task on a single dataset. The learned dictionary is then possibly biased towards the specific dataset and lacks of generalization abilities. In light of this, in this paper we propose a multitask sparsecoding approach by uncovering a shared subspace among heterogeneous datasets. The proposed multi-task coding strategy leverages the commonality benefit from different datasets. Moreover, our multi-task coding framework is capable of direct classification by incorporating label information. Experimental results show that the dictionary learned by our approach has more generalization abilities and our model performs better classification compared to the model learned from only one dataset or the model learned from simply pooling different datasets together.
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