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Image classification based on improved VLAD
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DOI：10.1007/s11042-015-2524-6.png)
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Abstract
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Recently, a coding scheme called vector of locally aggregated descriptors (VLAD) has got tremendous successes in large scale image retrieval due to its efficiency of compact representation. VLAD employs only the nearest neighbor visual word in dictionary to aggregate each descriptor feature. It has fast retrieval speed and high retrieval accuracy under small dictionary size. In this paper, we give three improved VLAD variations for image classification: first, similar to the bag of words (BoW) model, we count the number of descriptors belonging to each cluster center and add it to VLAD; second, in order to expand the impact of residuals, squared residuals are taken into account; thirdly, in contrast with one nearest neighbor visual word, we try to look for two nearest neighbor visual words for aggregating each descriptor. Experimental results on UIUC Sports Event, Corel 10 and 15 Scenes datasets show that the proposed methods outperform some state-of-the-art coding schemes in terms of the classification accuracy and computation speed.
Keywords:
Image classification
Scale-invariant feature transform
Vector of locally ggregated descriptors
K-means clustering algorithm
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