Randomized Distribution Feature for Image Classification

Local image features can be assumed to be drawn from an unknown distribution. For image classification, such features are compared through the histogram-based model or the metric-based model. By quantizing these local features into a set of histograms, the histogram-based model is convenient and has vectorial representation of image but information could be lost in vector quantization. Unlike the histogram-based model, the metric-based model estimates the metrics over the underlying distribution of local features immediately, achieving better predictive performance. However, the model requires higher computational cost and loses the benefit of vectorial representation of image.

Authors

• Hongming Shan
• Junping Zhang

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