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The expanding social network and multimedia technologies encourage more and more people to store and transmit information in visual format, such as image and video. However, the cost of this convenience brings about a shock to traditional video severs and exposes them under the risk of overloading. In the huge volume of online videos, there are a large amount of near-duplicate videos (NDVs). Although...
The main task of computer-aided diagnosis (CADx) is to differentiate the pathological stages to which each detected colorectal lesion belongs, especially to differentiate hyperplastic polyps, which are non-neoplastic and seldom show malignant potential, from neoplastic lesions, which are malignant or at risk for malignant transformation. If we could extract useful pattern information from detected...
Feature classification is an important part in computer-aided diagnosis of suspicious lesions. Currently there are many classifiers available, e.g., support vector machine (SVM), random forest (RF) and linear discriminant analysis (LDA). However, each of the classifiers has advantages and drawbacks and may show good performance in some cases and cannot show good classification in some other cases...
As a promising second reader for computed tomographic colonography (CTC) screening, computer-aided detection (CAD) of colorectal polyps have been explored extensively. In this paper, we present a random forest (RF) based CAD scheme. First, a thick colon wall called volumetric mucosa was extracted by segmentation method from CTC images. We then computed the first and second order derivatives to perform...
This paper considers the low-level feature modeling problem in image spam classification, in which most of the prevalent content based spam filters are shown to be inefficient because their OCR procedure are vulnerable to text obscuring attacks from spammers. We first built up a basic feature set through a low-level feature extraction process, and then proposed a stepwise regression method to determine...
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