This document describes the techniques used in and results of our project to apply social and information network analysis to the Kiva.org online microlending network. The Kiva network can be modeled as a set of bipartite graphs linking lenders to loans, lending teams to loans, and individual lenders to lending teams. Folding the lender-loan graph to create a graph of lenders linked by common l...

We present a technique to annotate multiple organs shown in 2-D abdominal/pelvic CT images using CBIR. This annotation task is motivated by our research interests in visual question-answering (VQA). We aim to apply results from this effort in Open-i, a multimodal biomedical search engine developed by the National Library of Medicine (NLM). Understanding visual content of biomedical images is a ...

Multiple instance learning (MIL) has attracted great attention recently in machine learning community. However, most MIL algorithms are very slow and cannot be applied to large datasets. In this paper, we propose a greedy strategy to speed up the multiple instance learning process. Our contribution is two fold. First, we propose a density ratio model, and show that maximizing a density ratio fu...

Klasifikasi merupakan serangkaian proses pembentukan model dari suatu objek ke dalam kelompok untuk memprediksi kelas yang belum diketahui sebelumnya. Modified K-Nearest Neighbor (MK-NN) salah satu metode klasifikasi pengembangan algoritma (K-NN) menambahkan validitas serta weight voting (pembobotan) mengatasi tingkat akurasi rendah K-NN. Penelitian ini bertujuan mengetahui hasil pengklasifikas...

Local binary pattern (LBP) is sensitive to noise. Noise-resistant LBP (NRLBP) addresses this problem by thresholding local neighboring pixels into three-valued states (i.e., 0, 1 and uncertain bits) recovering bits via an error-correction mechanism. In paper, we extend NRLBP deal with color images propose a robust image descriptor called Color context (CCBP). CCBP, employ scale neighbor progres...

Label fusion is a key step in multi-atlas based segmentation, which combines labels from multiple atlases to make the final decision. However, most of the current label fusion methods consider each voxel equally and independently during label fusion. In our point of view, however, different voxels act different roles in the way that some voxels might have much higher confidence in label determi...

Bagging is a simple and effective technique for generating an ensemble of classifiers. It is found there are a lot of redundant base classifiers in the original Bagging. We design a pruning approach to bagging for improving its generalization power. The proposed technique introduces the margin distribution based classification loss as the optimization objective and minimizes the loss on trainin...

The k-nearest neighbors (k-NN) classification rule is still an essential tool for computer vision applications, such as scene recognition. However, k-NN still features some major drawbacks, which mainly reside in the uniform voting among the nearest prototypes in the feature space. In this paper, we propose a new method that is able to learn the “relevance” of prototypes, thus classifying test ...

OBJECTIVES The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifie...