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      Görsel arama sonuçlarının çoklu örnekle öğrenme yöntemiyle yeniden sıralanması

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      Author
      Şener, Fadime
      Cinbiş, N. I.
      Duygulu-Şahin, Pınar
      Date
      2012-04
      Source Title
      20th Signal Processing and Communications Applications Conference, SIU
      Publisher
      IEEE
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      Bu çalışmada, çoklu öğrenme yöntemi ile metin tabanlı arama motorlarından elde edilen görsel sorgu sonuçlarını iyileştirmek için geliştirilmiş olan, zayıf denetimli öğrenen bir yöntem sunulmaktadır. Bu yöntemde arama motorundan dönen sonuçlar zayıf pozitif kabul edilerek, sorgu kategorisinden görüntü içermeyen negatif görüntüler de kullanılarak; çoklu örnekle öğrenme için torbalar oluşturulmaktadır. Bu torbalar ve veri kümesindeki örnekler arasında kurulan torba-örnek benzerliğinden yararlanarak; torbalar yeni bir örnek uzayına taşınmakta ve problem klasik bir denetimli öğrenme problemi haline getirilmektedir. Daha sonra, lineer destek vektör makinesi (DVM) kullanılarak her sorgu için sınıflandırma modelleri oluşturulmaktadır. Elde edilen sınıflandırma değerlerine göre görseller yeniden sıralanmış ve arama motorundan gelen sonuçların iyileştirildiği görülmüştür. Bu çerçevede, torba boyları arasında bir örüntü bulmak için yaptığımız deneyleri sunmaktayız. 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.
      Keywords
      Feature space
      Image search
      Image search engine
      Input datas
      Keyword queries
      Linear SVM
      Multiple instance learning
      Multiple instances
      Re-ranking
      Search engine results
      Similarity measure
      Supervised learning problems
      Web image search
      Input output programs
      Search engines
      Signal processing
      Learning systems
      Permalink
      http://hdl.handle.net/11693/28181
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2012.6204568
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      • Department of Computer Engineering 1371
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