58 Int. J. Web Science, Vol. 3, No. 1, 2017 Copyright © 2017 Inderscience Enterprises Ltd. Pseudo relevance feedback based on majority voting mechanism Mawloud Mosbah* and Bachir Boucheham Department of Informatics, Faculty of Sciences, University 20 Août 1955 of Skikda, Algeria Email: mos_nasa@hotmail.fr Email: bachirboucheham@gmail.com *Corresponding author Abstract: The pseudo relevance feedback mechanism has come to improve the performance of the CBIR systems before visualising the final results and without any user assistance. In this paper, we show the superiority of our proposed a pseudo relevance feedback scheme ‘majority voting algorithm’. The algorithm is compared to other approaches of the literature of that clustering materialised on two well known clustering algorithms namely: hierarchical agglomerative clustering method (HACM) and K-means and pseudo query reformulation materialised on pseudo query point movement, pseudo standard Rocchio formula and pseudo adaptive shifting query. Experiments are conducted on the heterogeneous Wang (COREL-1K) database and Google image engine using the colour moments as a signature. This work enables us to compare some pseudo relevance feedback techniques of the literature while the obtained results show the clear superiority of our proposed algorithm. Keywords: content-based image retrieval; CBIR; re-ranking; pseudo relevance feedback; majority voting re-ranking algorithm; hierarchical agglomerative clustering method; HACM, K-means; precision; recall. Reference to this paper should be made as follows: Mosbah, M. and Boucheham, B. (2017) ‘Pseudo relevance feedback based on majority voting mechanism’, Int. J. Web Science, Vol. 3, No. 1, pp.58–81. Biographical notes: Mawloud Mosbah is currently an Assistant Professor at the Informatics Department, University 20 Août 1955 of Skikda and member of LRES Laboratory. He is an External Reviewer of Digital Signal Processing Journal (Elsevier). He has worked as a Committee Member for WORLDCIST’17, FICC’18 conferences. He has some accepted and published works within international conferences (WORLDCIST’17, CERI’16, MTSR’15, ACIT’15, ICSIR’14, ICKRM’14, ICCIE’14, ICCVIA’14, WSCAR’14) and journals [Egyptian Informatics Journal (Elsevier), JIOS and IJWS]. Bachir Boucheham received his Doctor of Science (PhD) and HDR (Post-Doctoral degree for Research supervision) respectively in 2005 and 2009, both in Computer Science from Mentouri University of Constantine, Algeria. He is a Full Professor of Computer Science at the Department of Informatics, University of Skikda, Algeria and a member of the LRES research laboratory within the same university. His main areas of interest and expertise include pattern recognition, computer vision, image processing and retrieval, time series and signals processing and retrieval, data reduction and compression.