Pseudo relevance feedback based on majority voting mechanism

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.