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Image classification based on nearest neighbor basis vectors
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AI
DOI：10.1007/s11042-012-1289-4.png)
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Abstract
En 中文
Image classification can be roughly divided into two categories, i.e., scene recognition and object recognition. There are two important steps in object recognition: Dictionary Learning and Feature Coding. In order to get the best classification performance, the optimal dictionary learning method and feature coding strategy should be used simultaneously. However, researchers recently have found that feature coding was more important than dictionary learning when sparse coding scheme was employed. With a dictionary formed by a random sample of descriptors, satisfactory results were obtained. Inspired by the discovery, in this paper we propose an image classification method based on nearest neighbor basis vectors of the dictionary. Each descriptor of image is linearly represented by its several nearest neighbor basis vectors. We exploit the widely used Spatial Pyramid Matching model (SPM) in our paper and name our method Nearest Neighbor Basis Vectors Spatial Pyramid Matching (NNBVSPM). In the NNBVSPM, the dictionary is generated by standard k-means clustering algorithm and the feature is encoded by our soft inner product coding scheme. Experimental results on scene 15 dataset and uiuc sports event dataset show that the proposed scheme outperforms some state-of-the-art methods.
Keywords:
Image classification
Spatial pyramid matching
Scale-invariant feature transform
Nearest neighbor basis vector
Journal
IF:
3
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1.9W
Citations:
3.2W