Content-based image retrieval by dictionary of local feature descriptors

This paper describes a novel method of image key-point descriptor indexing and comparison used to speed up the process of content-based image retrieval as the main advantage of the dictionary-based representation is faster comparison of image descriptors sets in contrast to the standard list representation. The proposed method of descriptor representation allows to avoid initial learning process, and can be adjusted taking into consideration new examples. The presented method sorts and groups components of descriptors in the process of the dictionary creation. The ordered structure of the descriptors dictionary is well suited for quick comparison of images by comparing their dictionaries of descriptors or by comparing individual descriptors with the dictionary. This allows to skip a large part of operations during descriptors comparison between two images. In contrast to the standard dictionary, our method takes into account the standard deviation between the image descriptors. This is due to the fact that almost all descriptors generated for the points indicating the same areas of the image have different descriptors. Estimation of the similarity is based on the determined value of the standard deviation between descriptors. We assume that proposed method can speed up the process of descriptor comparison. It can be used with many solutions which require high-speed operations on the image e.g. robotics, or in software which computes panoramic photography from scrap images and in many others.