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Indirect immunofluorescence (IIF) imaging is a method used for detection of antinuclear auto-antibodies (ANA) for the diagnosis of autoimmune diseases. We present a feature extraction and classification scheme to classify the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of complementary features that are sensitive to staining pattern variations among classes. Our feature...
Learning from large, multi-class data sets poses great challenges to ensemble methods. The weak learner condition makes the conventional method inappropriate to handle multi-class classification, which leads to early termination of the training process. Also, elongated training time makes learning from large data set infeasible. To circumvent these issues, we present a novel method that integrates...
In this paper we present a cascade-based framework for object detection in which the node classifiers are trained by a learning algorithm based on ranking instead of classification error. Such an approach is particularly suited for facing the asymmetry between positive and negative class, that is a huge problem in object detection applications. Other methods focused on this problem and previously...
In this paper, we present four image descriptors for HEp-2 cell staining patterns classification, including LBP, Gabor, DCT, and a global appearance statistical descriptor. A multiclass boosting SVM algorithm is proposed to integrate these descriptors together: (1) within each boosting round, four multiclass posterior probability SVMs are trained corresponding to four descriptors, and then combined...
Significant progress has been made towards learning a generalized offline object detector. However, when a generalized offline detector is applied on new datasets, it often misses some instances of the object or produces false alarms in the background scene. we propose a novel and efficient incremental learning method, which improves the performance of an offline trained detector. Our approach adjusts...
This paper presents a novel method for pose classification using fusion of visual and thermal infrared(IR) images. We propose a novel tree structure multi-class classification scheme with visual and IR sub-classifiers. These sub-classifiers are different from the conventional one-against-all or one-against-one strategies, where we handle the multi-class problem directly. We propose to use an accuracy...
We propose a generic framework to handle missing weak classifiers at prediction time in a boosted cascade. The contribution is a probabilistic formulation of the cascade structure that considers the uncertainty introduced by missing weak classifiers. This new formulation involves two problems: 1) the approximation of posterior probabilities on each level and 2) the computation of thresholds on these...
This paper introduces CMV100, a new research dataset for people tracking and re-identification in sparse camera networks. Baseline methods for reidentification performance analysis are also proposed. The dataset consist of over 400 indoor video sequences in total. The number of visually distinctive human objects is 100, and each person appears in three different views on average and in five at maximum...
To deal with the drifting issue in visual tracking, we propose an Online Transfer Boosting (OTB) algorithm that transfers knowledge from three different source domains to the target domain to improve the performance of the online classifier used in tracking-by-detection. In particular, the OTB algorithm integrates three types of knowledge by: (1) transferring prior knowledge from the first frame using...
In this paper, we consider the multi-camera tracking and the camera active control (pan and tilt). Auction mechanism from economics is developed to choose the best available camera. By modeling the camera bids with prior knowledge of the camera homographies, the system can “think” ahead to perform necessary panning or tilting operations. The uncertainties of homographies are considered inherently...
We introduce a boosting framework for multiple instance learning (MIL) with varied aggregation of instances. In this framework, a diverse set of aggregation functions can be used to refine the notion of a positive bag for multiple instance learning. We investigate the effect of a wide range of orness in aggregation, using ordered weighted averaging. Thus, we obtain a new notion of a positive bag,...
This paper addresses the problem of binary classifier learning when the training data is imbalanced, i.e. the samples of the two classes have significantly different cardinality. We investigate two different cost-sensitive approaches in the conditional mutual information (CMI) based weak classifier selection procedure using histogram descriptors. The first method uses CMI for classifier selection,...
To identify microorganisms is of utmost importance in various applications such as medical science and pharmaceutical industry. The technique of Raman spec-troscopy is particularly useful in this scenario, since it extracts a high-dimensional molecular fingerprint from samples at hand. Instead of using the complete spectrum, it is often sensible to concentrate on a small number of discriminative dimensions...
We extend the PCT (Pseudo Census Transform)-based appearance model [3] to ranking-based appearance model for face alignment. The PCT-based weak ranking function is learned using RankSVM, and the ranking appearance model (RAM) is constructed in a boosting manner. Experiments show that the PCT-based RAM is more robust and generalize better than the PCT-based boosted appearance model (BAM). The PCT-RAM...
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