Browsing by Subject "Multiple instances"

Now showing 1 - 3 of 3

Open Access
Ensemble of multiple instance classifiers for image re-ranking
(Elsevier Ltd, 2014) Sener F.; Ikizler-Cinbis, N.
Text-based image retrieval may perform poorly due to the irrelevant and/or incomplete text surrounding the images in the web pages. In such situations, visual content of the images can be leveraged to improve the image ranking performance. In this paper, we look into this problem of image re-ranking and propose a system that automatically constructs multiple candidate "multi-instance bags (MI-bags)", which are likely to contain relevant images. These automatically constructed bags are then utilized by ensembles of Multiple Instance Learning (MIL) classifiers and the images are re-ranked according to the final classification responses. Our method is unsupervised in the sense that, the only input to the system is the text query itself, without any user feedback or annotation. The experimental results demonstrate that constructing multiple instance bags based on the retrieval order and utilizing ensembles of MIL classifiers greatly enhance the retrieval performance, achieving on par or better results compared to the state-of-the-art. © 2014 Elsevier B.V.
Open Access
Görsel arama sonuçlarının çoklu örnekle öğrenme yöntemiyle yeniden sıralanması
(IEEE, 2012-04) Şener, Fadime; Cinbiş, N. I.; Duygulu-Şahin, Pınar
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.
Open Access
Running multiple instances of the distributed coordination function for air-time fairness in multi-rate WLANs
(IEEE, 2013) Yazici, M. A.; Akar, N.
Conventional multi-rate IEEE 802.11 Wireless LANs (WLANs) are associated with the so-called performance anomaly to describe the phenomenon of high bit rate nodes being dragged down by slower nodes. This anomaly is known to be an impediment to obtaining high cumulative throughputs despite the employment of effective link adaptation mechanisms. To cope with the performance anomaly, air-time fairness has been proposed as an alternative to throughput fairness, the latter being a main characteristic of the IEEE 802.11 Distributed Coordination Function (DCF). In this paper, we propose a novel distributed air-time fair MAC (Medium Access Control) without having to change the operation of the conventional DCF. In the proposed MAC, each node in the system runs multiple instances of the conventional DCF back-off algorithm where the number of DCF instances for the nodes can be chosen in a distributed manner. Both analytical and simulation-based results are provided to validate the effectiveness of the proposed air-time fair MAC.