Perceptually Inspired Normalized Conditional Compression Distance

Autor:	Nikvand, Nima, Wang, Zhou, Fernando, Xavier, Farjow, Wisam
Rok vydání:	2018
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	Image similarity measurement is a common issue in a broad range of applications in image processing, recognition, classification and retrieval. Conventional image similarity measures are often limited to specific applications and cannot be applied in general scenarios. The theory of Kolmogorov complexity provides a universal framework for a generic similarity metric based on information distance between objects. Normalized Information Distance (NID) has been shown to be a valid and universal distance metric applicable in measurement of similarity of any two objects, and has been successfully applied to a wide range of applications in the past. The difficulty of NID lies in the non-computable nature of the Kolmogorov complexity, and thus approximation has to be applied in practice. Here we propose a perceptually-inspired Normalized Conditional Compression Distance (NCCD) measure by using the Divisive Normalization Transform (DNT) as a means to model the non-linear behavior of the Human Visual System (HVS) in reducing statistical dependencies of visual signals for efficient representation, and show that this perceptual extension of NID can be used in a wide range of image processing applications, including texture classification and face recognition. Comment: I have received major revisions from IEEE Access magazine and I have decided not to resubmit the paper to that magazine. I will not revise and resubmit in the foreseeable future.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1810.00059 Zobrazit plný text záznamu View this record from Arxiv