Unsupervised Distributed Feature Selection for Multi-view Object Recognition

Object recognition accuracy can be improved when information frommultiple views is integrated, but information in each view can oftenbe highly redundant. We consider the problem of distributed objectrecognition or indexing from multiple cameras, where thecomputational power available at each camera sensor is limited andcommunication between sensors is prohibitively expensive. In thisscenario, it is desirable to avoid sending redundant visual featuresfrom multiple views, but traditional supervised feature selectionapproaches are inapplicable as the class label is unknown at thecamera. In this paper we propose an unsupervised multi-view featureselection algorithm based on a distributed compression approach.With our method, a Gaussian Process model of the joint viewstatistics is used at the receiver to obtain a joint encoding of theviews without directly sharing information across encoders. Wedemonstrate our approach on recognition and indexing tasks withmulti-view image databases and show that our method comparesfavorably to an independent encoding of the features from eachcamera

Probabilistic models for multi-view semi-supervised learning and coding

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 146-160).This thesis investigates the problem of classification from multiple noisy sensors or modalities. Examples include speech and gesture interfaces and multi-camera distributed sensor networks. Reliable recognition in such settings hinges upon the ability to learn accurate classification models in the face of limited supervision and to cope with the relatively large amount of potentially redundant information transmitted by each sensor or modality (i.e., view). We investigate and develop novel multi view learning algorithms capable of learning from semi-supervised noisy sensor data, for automatically adapting to new users and working conditions, and for performing distributed feature selection on bandwidth limited sensor networks. We propose probabilistic models built upon multi-view Gaussian Processes (GPs) for solving this class of problems, and demonstrate our approaches for solving audio-visual speech and gesture, and multi-view object classification problems. Multi-modal tasks are good candidates for multi-view learning, since each modality provides a potentially redundant view to the learning algorithm. On audio-visual speech unit classification, and user agreement recognition using spoken utterances and head gestures, we demonstrate that multi-modal co-training can be used to learn from only a few labeled examples in one or both of the audio-visual modalities. We also propose a co-adaptation algorithm, which adapts existing audio-visual classifiers to a particular user or noise condition by leveraging the redundancy in the unlabeled data. Existing methods typically assume constant per-channel noise models.(cont.) In contrast we develop co-training algorithms that are able to learn from noisy sensor data corrupted by complex per-sample noise processes, e.g., occlusion common to multi sensor classification problems. We propose a probabilistic heteroscedastic approach to co-training that simultaneously discovers the amount of noise on a per-sample basis, while solving the classification task. This results in accurate performance in the presence of occlusion or other complex noise processes. We also investigate an extension of this idea for supervised multi-view learning where we develop a Bayesian multiple kernel learning algorithm that can learn a local weighting over each view of the input space. We additionally consider the problem of distributed object recognition or indexing from multiple cameras, where the computational power available at each camera sensor is limited and communication between cameras is prohibitively expensive. In this scenario, it is desirable to avoid sending redundant visual features from multiple views. Traditional supervised feature selection approaches are inapplicable as the class label is unknown at each camera. In this thesis, we propose an unsupervised multi-view feature selection algorithm based on a distributed coding approach. With our method, a Gaussian Process model of the joint view statistics is used at the receiver to obtain a joint encoding of the views without directly sharing information across encoders. We demonstrate our approach on recognition and indexing tasks with multi-view image databases and show that our method compares favorably to an independent encoding of the features from each camera.by C. Mario Christoudias.Ph.D

Firefighters and on-duty deaths from coronary heart disease: a case control study

BACKGROUND: Coronary heart disease (CHD) is responsible for 45% of on-duty deaths among United States firefighters. We sought to identify occupational and personal risk factors associated with on-duty CHD death. METHODS: We performed a case-control study, selecting 52 male firefighters whose CHD deaths were investigated by the National Institute for Occupational Safety and Health. We selected two control populations: 51 male firefighters who died of on-duty trauma; and 310 male firefighters examined in 1996/1997, whose vital status and continued professional activity were re-documented in 1998. RESULTS: The circadian pattern of CHD deaths was associated with emergency response calls: 77% of CHD deaths and 61% of emergency dispatches occurred between noon and midnight. Compared to non-emergency duties, fire suppression (OR = 64.1, 95% CI 7.4–556); training (OR = 7.6, 95% CI 1.8–31.3) and alarm response (OR = 5.6, 95% CI 1.1–28.8) carried significantly higher relative risks of CHD death. Compared to the active firefighters, the CHD victims had a significantly higher prevalence of cardiovascular risk factors in multivariate regression models: age ≥ 45 years (OR 6.5, 95% CI 2.6–15.9), current smoking (OR 7.0, 95% CI 2.8–17.4), hypertension (OR 4.7, 95% CI 2.0–11.1), and a prior diagnosis of arterial-occlusive disease (OR 15.6, 95% CI 3.5–68.6). CONCLUSIONS: Our findings strongly support that most on-duty CHD fatalities are work-precipitated and occur in firefighters with underlying CHD. Improved fitness promotion, medical screening and medical management could prevent many of these premature deaths

Domain Adaptation for Microscopy Imaging

Electron and Light Microscopy imaging can now deliver high-quality image stacks of neural structures. However, the amount of human annotation effort required to analyze them remains a major bottleneck. While Machine Learning algorithms can be used to help automate this process, they require training data, which is time-consuming to obtain manually, especially in image stacks. Furthermore, due to changing experimental conditions, successive stacks often exhibit differences that are severe enough to make it difficult to use a classifier trained for a specific one on another. This means that this tedious annotation process has to be repeated for each new stack. In this paper we present a domain adaptation algorithm that addresses this issue by effectively leveraging labeled examples across different acquisitions and significantly reducing the annotation requirements. Our approach can handle complex, non-linear image feature transformations and scales to large microscopy datasets that often involve high-dimensional feature spaces and large 3D data volumes. We evaluate our approach on four challenging Electron and Light Microscopy applications that exhibit very different image modalities and where annotation is very costly. Across all applications we achieve a significant improvement over the state-of-the-art Machine Learning methods and demonstrate our ability to greatly reduce human annotation effort

Real-time landing place assessment in man-made environments

We propose a novel approach to the real-time landing site detection and assessment in unconstrained man-made environments using passive sensors. Because this task must be performed in a few seconds or less, existing methods are often limited to simple local intensity and edge variation cues. By contrast, we show how to efficiently take into account the potential sites' global shape, which is a critical cue in man-made scenes. Our method relies on a new segmentation algorithm and shape regularity measure to look for polygonal regions in video sequences. In this way, we enforce both temporal consistency and geometric regularity, resulting in very reliable and consistent detections. We demonstrate our approach for the detection of landable sites such as rural fields, building rooftops and runways from color and infrared monocular sequences significantly outperforming the state-of-the-art

Real-time landing place assessment in man-made environments

We propose a novel approach to the real-time landing site detection and assessment in unconstrained man-made environments using passive sensors. Because this task must be performed in a few seconds or less, existing methods are often limited to simple local intensity and edge variation cues. By contrast, we show how to efficiently take into account the potential sites' global shape, which is a critical cue in man-made scenes. Our method relies on a new segmentation algorithm and shape regularity measure to look for polygonal regions in video sequences. In this way, we enforce both temporal consistency and geometric regularity, resulting in very reliable and consistent detections. We demonstrate our approach for the detection of landable sites such as rural fields, building rooftops and runways from color and infrared monocular sequences significantly outperforming the state-of-the-art

Measurement of the B0_s semileptonic branching ratio to an orbitally excited D_s** state, Br(B0_s -> Ds1(2536) mu nu)

In a data sample of approximately 1.3 fb-1 collected with the D0 detector between 2002 and 2006, the orbitally excited charm state D_s1(2536) has been observed with a measured mass of 2535.7 +/- 0.6 (stat) +/- 0.5 (syst) MeV via the decay mode B0_s -> D_s1(2536) mu nu X. A first measurement is made of the branching ratio product Br(b(bar) -> D_s1(2536) mu nu X).Br(D_s1(2536)->D* K0_S). Assuming that D_s1(2536) production in semileptonic decay is entirely from B0_s, an extraction of the semileptonic branching ratio Br(B0_s -> D_s1(2536) mu nu X) is made.Comment: 7 pages, 2 figures, LaTeX, version with minor changes as accepted by Phys. Rev. Let