Title :
Application of Relevance Feedback in Content Based Image Retrieval Using Gaussian Mixture Models
Author :
Marakakis, Apostolos ; Galatsanos, Nikolaos ; Likas, Aristidis ; Stafylopatis, Andreas
Author_Institution :
Sch. of Electr. & Comput. Eng., Nat. Tech. Univ. of Athens, Athens
Abstract :
In this paper a relevance feedback (RF) approach for content based image retrieval (CBIR) is described and evaluated. The approach uses Gaussian mixture (GM) models of the image features and a query that is updated in a probabilistic manner. This update reflects the preferences of the user and is based on the models of both positive and negative feedback images. Retrieval is based on a recently proposed distance measure between probability density functions (pdfs), which can be computed in closed form for GM models. The proposed approach takes advantage of the form of this distance measure and updates it very efficiently based on the models of the user specified relevant and irrelevant images. For evaluation purposes, comparative experimental results are presented that demonstrate the merits of the proposed methodology.
Keywords :
Gaussian processes; content-based retrieval; image retrieval; relevance feedback; Gaussian mixture models; content based image retrieval; negative feedback images; positive feedback images; probability density functions; relevance feedback; Artificial intelligence; Content based retrieval; Context modeling; Humans; Image databases; Image retrieval; Information retrieval; Negative feedback; Probability density function; Radio frequency;
Conference_Titel :
Tools with Artificial Intelligence, 2008. ICTAI '08. 20th IEEE International Conference on
Conference_Location :
Dayton, OH
Print_ISBN :
978-0-7695-3440-4
DOI :
10.1109/ICTAI.2008.110