Ambiguous Proximity Distribution
This work addresses image representation for medical image analysis, but it is incremental as it builds on existing bag-of-features methods.
The paper tackled the problem of soft assignment of visual words for proximity distribution in bag-of-features image representation by proposing visual word contribution functions to model ambiguous proximity distributions, resulting in performance that is better or comparable to state-of-the-art methods on medical image classification and retrieval datasets.
Proximity Distribution Kernel is an effective method for bag-of-featues based image representation. In this paper, we investigate the soft assignment of visual words to image features for proximity distribution. Visual word contribution function is proposed to model ambiguous proximity distributions. Three ambiguous proximity distributions is developed by three ambiguous contribution functions. The experiments are conducted on both classification and retrieval of medical image data sets. The results show that the performance of the proposed methods, Proximity Distribution Kernel (PDK), is better or comparable to the state-of-the-art bag-of-features based image representation methods.