Ranking System for Image Database Using Special Type of Markov Chain
Hiromitsu Hama, Thi Thi Zin, Pyke Tin · 2008
In this paper, we explore and examine a new novel approach to image ranking systems based on some special types of Markov chain along with new concepts of popularity and relevancy measures for image database. To be specific, this approach introduces a family of special Markov chain models in which serial correlations are explicitly involved so that we can use them as correlations among the images. By using these models, we develop a ranking function for the image database. On the other hand, 'popularity' and 'relevancy' concepts are introduced and used for developing an alternative ranking function for the database. In the process of developing ranking functions we use a method of queue-based stochastic difference equations. We then blend two ranking functions, to propose a new ranking system for searching order of image database. Since the proposed ranking system considers concepts of correlations, popularity and relevancy altogether, it is beneficial to a modern search engine for investigating behavior and effects of those parameters on the search results. Some illustrative examples and simulation results are presented with reference to a real world application domain.