Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
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TL;DR: This paper creates the most complete dataset, focusing on accelerometer sensors, and conducts an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition.
Abstract: Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.
162 citations
Cites methods from "Pattern Recognition and Machine Lea..."
...Naïve Bayes (NB) is a powerful probabilistic classifier employing a simplified version of Bayes formula to decide on a class of a new instance [61]....
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23 Jun 2014
TL;DR: A multi-view depthmap estimation approach aimed at adaptively ascertaining the pixel level data associations between a reference image and all the elements of a source image set, and the linear computational and storage requirements of the formulation, as well as its inherent parallelism enables an efficient and scalable GPU-based implementation.
Abstract: We propose a multi-view depthmap estimation approach aimed at adaptively ascertaining the pixel level data associations between a reference image and all the elements of a source image set. Namely, we address the question, what aggregation subset of the source image set should we use to estimate the depth of a particular pixel in the reference image? We pose the problem within a probabilistic framework that jointly models pixel-level view selection and depthmap estimation given the local pairwise image photoconsistency. The corresponding graphical model is solved by EM-based view selection probability inference and PatchMatch-like depth sampling and propagation. Experimental results on standard multi-view benchmarks convey the state-of-the art estimation accuracy afforded by mitigating spurious pixel level data associations. Additionally, experiments on large Internet crowd sourced data demonstrate the robustness of our approach against unstructured and heterogeneous image capture characteristics. Moreover, the linear computational and storage requirements of our formulation, as well as its inherent parallelism, enables an efficient and scalable GPU-based implementation.
162 citations
Cites background or methods from "Pattern Recognition and Machine Lea..."
...The probability of each hidden variable q(Z l ) can be efficiently inferred by forward-backward algorithm [3]....
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...To optimize over qm(Z), the standard solution [3] is log (qm(Z)) = E\m[log (P (X,θ,Z))] + const, where E\m is the expectation of log (P (X,θ,Z)) taken over all variables not in qm(Z) [3]....
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...We put restrictions on the family of distributions q(Z,θ), assuming that it is factorizable into a set of distributions ([3]):...
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...Variational inference is to consider a restricted family of distributions q(Z,θ) and then seek the member of this family to approximate the real posterior distribution P (Z,θ|X), in the sense that the KL divergence between these two is minimized [3]....
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...Hence, we propose a Monte Carlo based approximation [3]....
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23 Jun 2013
TL;DR: A novel approach for video parsing and simultaneous online learning of dominant and anomalous behaviors in surveillance videos using densely constructed spatio-temporal video volumes, which is ultimately capable of simultaneously modeling high-level behaviors as well as low-level spatial, temporal and spatiospecies pixel level changes.
Abstract: We present a novel approach for video parsing and simultaneous online learning of dominant and anomalous behaviors in surveillance videos. Dominant behaviors are those occurring frequently in videos and hence, usually do not attract much attention. They can be characterized by different complexities in space and time, ranging from a scene background to human activities. In contrast, an anomalous behavior is defined as having a low likelihood of occurrence. We do not employ any models of the entities in the scene in order to detect these two kinds of behaviors. In this paper, video events are learnt at each pixel without supervision using densely constructed spatio-temporal video volumes. Furthermore, the volumes are organized into large contextual graphs. These compositions are employed to construct a hierarchical codebook model for the dominant behaviors. By decomposing spatio-temporal contextual information into unique spatial and temporal contexts, the proposed framework learns the models of the dominant spatial and temporal events. Thus, it is ultimately capable of simultaneously modeling high-level behaviors as well as low-level spatial, temporal and spatio-temporal pixel level changes.
162 citations
TL;DR: A novel tissue classification approach which may save clinician's time by avoiding chromoendoscopy, a time-consuming staining procedure using indigo carmine, and a database of colonoscopic videos showing gastrointestinal lesions with ground truth collected from both expert image inspection and histology is proposed.
Abstract: We have developed a technique to study how good computers can be at diagnosing gastrointestinal lesions from regular (white light and narrow banded) colonoscopic videos compared to two levels of clinical knowledge (expert and beginner). Our technique includes a novel tissue classification approach which may save clinician's time by avoiding chromoendoscopy, a time-consuming staining procedure using indigo carmine. Our technique also discriminates the severity of individual lesions in patients with many polyps, so that the gastroenterologist can directly focus on those requiring polypectomy. Technically, we have designed and developed a framework combining machine learning and computer vision algorithms, which performs a virtual biopsy of hyperplastic lesions, serrated adenomas and adenomas. Serrated adenomas are very difficult to classify due to their mixed/hybrid nature and recent studies indicate that they can lead to colorectal cancer through the alternate serrated pathway. Our approach is the first step to avoid systematic biopsy for suspected hyperplastic tissues. We also propose a database of colonoscopic videos showing gastrointestinal lesions with ground truth collected from both expert image inspection and histology. We not only compare our system with the expert predictions, but we also study if the use of 3D shape features improves classification accuracy, and compare our technique's performance with three competitor methods.
162 citations
Cites background from "Pattern Recognition and Machine Lea..."
...Recent studies have proposed the so-called Kernel-PCA [74] signature for the recognition of shapes using depth sensors [75]....
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...Then the Kernel-PCA descriptor is composed of the L largest eigenvalues of the kernel matrix....
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...Kernel-PCA eigenvalues are invariant to rotation and translation....
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...We concatenate both Shape-DNA and Kernel-PCA to create a single 3D shape descriptor that we use for classification....
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...2) 3D Cloud Signatures with Kernel-PCA: In some cases, mainly due to the lack of texture, SfM gives a reconstruction with a poor mesh resolution....
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Proceedings Article•
12 Aug 2015TL;DR: The extent to which cyber security incidents, such as those referenced by Verizon in its annual Data Breach Investigations Reports (DBIR), can be predicted based on externally observable properties of an organization's network is characterized.
Abstract: In this study we characterize the extent to which cyber security incidents, such as those referenced by Verizon in its annual Data Breach Investigations Reports (DBIR), can be predicted based on externally observable properties of an organization's network We seek to proactively forecast an organization's breaches and to do so without cooperation of the organization itself To accomplish this goal, we collect 258 externally measurable features about an organization's network from two main categories: mismanagement symptoms, such as misconfigured DNS or BGP within a network, and malicious activity time series, which include spam, phishing, and scanning activity sourced from these organizations Using these features we train and test a Random Forest (RF) classifier against more than 1,000 incident reports taken from the VERIS community database, Hackmageddon, and the Web Hacking Incidents Database that cover events from mid-2013 to the end of 2014 The resulting classifier is able to achieve a 90% True Positive (TP) rate, a 10% False Positive (FP) rate, and an overall 90% accuracy
161 citations