Browsing by Author "Ozan, Ezgi Can"

Now showing items 1-5 of 5

    • An optimized k-NN approach for classification on imbalanced datasets with missing data 

      Ozan, Ezgi Can; Riabchenko, Ekaterina; Kiranyaz, Serkan; Gabbouj, Moncef ( Springer Verlag , 2016 , Conference Paper)
      In this paper, we describe our solution for the machine learning prediction challenge in IDA 2016. For the given problem of 2-class classification on an imbalanced dataset with missing data, we first develop an imputation ...

    • Competitive Quantization for Approximate Nearest Neighbor Search 

      Ozan, Ezgi Can; Kiranyaz, Serkan; Gabbouj, Moncef ( IEEE Computer Society , 2016 , Article)
      In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is ...

    • Extended quantum cuts for unsupervised salient object extraction 

      Aytekin, Caglar; Ozan, Ezgi Can; Kiranyaz, Serkan; Gabbouj , Moncef ( Springer New York LLC , 2017 , Article)
      In this manuscript, an unsupervised salient object extraction algorithm is proposed for RGB and RGB-Depth images. Saliency estimation is formulated as a foreground detection problem. To this end, Quantum-Cuts (QCUT), a ...

    • Joint K-Means quantization for Approximate Nearest Neighbor Search 

      Ozan, Ezgi Can; Kiranyaz, Serkan; Gabbouj, Moncef ( Institute of Electrical and Electronics Engineers Inc. , 2016 , Conference Paper)
      Recently, Approximate Nearest Neighbor (ANN) Search has become a very popular approach for similarity search on large-scale datasets. In this paper, we propose a novel vector quantization method for ANN, which introduces ...

    • Self-organizing binary encoding for approximate nearest neighbor search 

      Ozan, Ezgi Can; Kiranyaz, Serkan; Gabbouj, Moncef; Hu, Xiaohua ( European Signal Processing Conference, EUSIPCO , 2016 , Conference Paper)
      Approximate Nearest Neighbor (ANN) search for indexing and retrieval has become very popular with the recent growth of the databases in both size and dimension. In this paper, we propose a novel method for fast approximate ...