Multiple instance learning for re-ranking of web image search results Görsel arama sonuçlarinin çoklu örnekle öǧrenme yöntemi̇yle yeni̇den siralanmasi

摘要

In this study, we propose a weakly-supervised multiple instance learning (MIL) method to improve the results of text-based image search engines. In this approach, ranked image list of search engine for a keyword query is treated as weak-positive input data, and with additional negative input data, multiple instance learning bags are constructed. Then, Multiple Instance problem is converted to a standard supervised learning problem by mapping each bag into a feature space defined by instances in training bags using a bag-instance similarity measure. At the end, linear SVM is used to construct a classifier to re-rank keyword-based image search data. Based on the classification scores, we re-rank the images and improve precision over the search engine results. In this respect, we also present our experiments conducted to find a pattern for multiple instance bag sizes to obtain better average precision. © 2012 IEEE.