Encoding Optimization Using Nearest Neighbor Descriptor

The Bag-of-words framework is probably one of the best models used in image classification. In this model, coding plays a very important role in the classification process. There are many coding methods that have been proposed to encode images in different ways. The relationship between different codewords is studied, but the relationship among descriptors is not fully discovered. In this work, we aim to draw a relationship between descriptors, and propose a new method that can be used with other coding methods to improve the performance. The basic idea behind this is encoding the descriptor not only with its nearest codewords but also with the codewords of its nearest neighboring descriptors. Experiments on several benchmark datasets show that even using this simple relationship between the descriptors helps to improve coding methods.