We present a data mining approach for profiling bank clients in order to support the process of detection of anti-money laundering operations. We first present the overall system architecture, and then focus on the relevant component for this paper. We detail the experiments performed on real world data from a financial institution, which allowed us to group clients in clusters and then generate a set of classification rules. We discuss the relevance of the founded client profiles and of the generated classification rules. According to the defined overall agent-based architecture, these rules will be incorporated in the knowledge base of the intelligent agents responsible for the signaling of suspicious transactions.
Considering the data transformation process, which was especially critical in our work, in @cite_6 , a discretization process was also applied to data set in order to find a more adequate set of clusters. Resources are mapped to n+2 dimensional Euclidean space, n being the customer’s attributes, one dimension for time and another for transactions. Customers’ transactions are projected according to time, accumulating transactions and its frequency to create a histogram. Clusters are created based on segments of the histogram. Analyses of local and global correlations are then applied to detect suspicious patterns. A good way to analyze individual behavior and or group behavior is to examine their operations to detect suspicious behaviors related to abnormal peaks on the histogram. However, when it is necessary to analyze a big number of clients and transactions over a long period of time, it may become difficult to detect suspicious cases, since there might be few peaks or none at all in the histogram.
@cite_6
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We propose a new approach to appearance-based loop detection for mobile robots, using three-dimensional (3D) laser scans. Loop detection is an important problem in the simultaneous localization and mapping (SLAM) domain, and, because it can be seen as the problem of recognizing previously visited places, it is an example of the data association problem. Without a flat-floor assumption, two-dimensional laser-based approaches are bound to fail in many cases. Two of the problems with 3D approaches that we address in this paper are how to handle the greatly increased amount of data and how to efficiently obtain invariance to 3D rotations. We present a compact representation of 3D point clouds that is still discriminative enough to detect loop closures without false positives (i.e., detecting loop closure where there is none). A low false-positive rate is very important because wrong data association could have disastrous consequences in a SLAM algorithm. Our approach uses only the appearance of 3D point clouds to detect loops and requires no pose information. We exploit the normal distributions transform surface representation to create feature histograms based on surface orientation and smoothness. The surface shape histograms compress the input data by two to three orders of magnitude. Because of the high compression rate, the histograms can be matched efficiently to compare the appearance of two scans. Rotation invariance is achieved by aligning scans with respect to dominant surface orientations. We also propose to use expectation maximization to fit a gamma mixture model to the output similarity measures in order to automatically determine the threshold that separates scans at loop closures from nonoverlapping ones. We discuss the problem of determining ground truth in the context of loop detection and the difficulties in comparing the results of the few available methods based on range information. Furthermore, we present quantitative performance evaluations using three real-world data sets, one of which is highly self-similar, showing that the proposed method achieves high recall rates (percentage of correctly identified loop closures) at low false-positive rates in environments with different characteristics. © 2009 Wiley Periodicals, Inc. In this paper we study the problem of constructing histograms from high-speed time-changing data streams. Learning in this context requires the ability to process examples once at the rate they arrive, maintaining a histogram consistent with the most recent data, and forgetting out-date data whenever a change in the distribution is detected. To construct histogram from high-speed data streams we use the two layer structure used in the Partition Incremental Discretization (PiD) algorithm. Our contribution is a new method to detect whenever a change in the distribution generating examples occurs. The base idea consists of monitoring distributions from two different time windows: the reference time window, that reflects the distribution observed in the past; and the current time window reflecting the distribution observed in the most recent data. We compare both distributions and signal a change whenever they are greater than a threshold value, using three different methods: the Entropy Absolute Difference, the Kullback-Leibler divergence and the Cosine Distance. The experimental results suggest that Kullback-Leibler divergence exhibit high probability in change detection, faster detection rates, with few false positives alarms. We propose a novel approach to both learning and detecting local contour-based representations for mid-level features. Our features, called sketch tokens, are learned using supervised mid-level information in the form of hand drawn contours in images. Patches of human generated contours are clustered to form sketch token classes and a random forest classifier is used for efficient detection in novel images. We demonstrate our approach on both top-down and bottom-up tasks. We show state-of-the-art results on the top-down task of contour detection while being over 200x faster than competing methods. We also achieve large improvements in detection accuracy for the bottom-up tasks of pedestrian and object detection as measured on INRIA and PASCAL, respectively. These gains are due to the complementary information provided by sketch tokens to low-level features such as gradient histograms. Face detection systems have recently achieved high detection rates and real-time performance. However, these methods usually rely on a huge training database (around 5,000 positive examples for good performance). While such huge databases may be feasible for building a system that detects a single object, it is obviously problematic for scenarios where multiple objects (or multiple views of a single object) need to be detected. Indeed, even for multi-viewface detection the performance of existing systems is far from satisfactory. In this work we focus on the problem of learning to detect objects from a small training database. We show that performance depends crucially on the features that are used to represent the objects. Specifically, we show that using local edge orientation histograms (EOH) as features can significantly improve performance compared to the standard linear features used in existing systems. For frontal faces, local orientation histograms enable state of the art performance using only a few hundred training examples. For profile view faces, local orientation histograms enable learning a system that seems to outperform the state of the art in real-time systems even with a small number of training examples. The histogram of oriented gradients (HOG) is widely used for image description and proves to be very effective. In many vision problems, rotation-invariant analysis is necessary or preferred. Popular solutions are mainly based on pose normalization or learning, neglecting some intrinsic properties of rotations. This paper presents a method to build rotation-invariant HOG descriptors using Fourier analysis in polar spherical coordinates, which are closely related to the irreducible representation of the 2D 3D rotation groups. This is achieved by considering a gradient histogram as a continuous angular signal which can be well represented by the Fourier basis (2D) or spherical harmonics (3D). As rotation-invariance is established in an analytical way, we can avoid discretization artifacts and create a continuous mapping from the image to the feature space. In the experiments, we first show that our method outperforms the state-of-the-art in a public dataset for a car detection task in aerial images. We further use the Princeton Shape Benchmark and the SHREC 2009 Generic Shape Benchmark to demonstrate the high performance of our method for similarity measures of 3D shapes. Finally, we show an application on microscopic volumetric data. Attributes of a relation are not typically independent. Multidimensional histograms can be an effective tool for accurate multiattribute query selectivity estimation. In this paper, we introduce STHoles, a “workload-aware” histogram that allows bucket nesting to capture data regions with reasonably uniform tuple density. STHoles histograms are built without examining the data sets, but rather by just analyzing query results. Buckets are allocated where needed the most as indicated by the workload, which leads to accurate query selectivity estimations. Our extensive experiments demonstrate that STHoles histograms consistently produce good selectivity estimates across synthetic and real-world data sets and across query workloads, and, in many cases, outperform the best multidimensional histogram techniques that require access to and processing of the full data sets during histogram construction. This paper presents a novel approach to face recognition, named Multi-resolution Histograms of Local Variation Patterns (MHLVP), in which face images are represented as the concatenation of the local spatial histogram of local variation patterns computed from the multi-resolution Gabor features. For a face image with abundant texture and shape information, aGabor feature map(GFM) is computed by convolving the image with each of the forty multi-scale and multi-orientation Gabor filters. Each GFM is then divided into small non-overlapping regions to enhance its shape information, and then Local Binary Pattern (LBP) histograms are extracted for each region and concatenated into a feature histogram to enhance the texture information in the specific GFM. Further more, all of the feature histograms extracted from the forty GFMs are further concatenated into a single feature histogram as the final representation of the given face image. Eventually, the identification is achieved by histogram intersection operation. Our experimental results on FERET face databases show that the proposed method performs terrifically better than the performance of some classical results including the best results in FERET'97. Suspicious transaction detection is used to report banking transactions that may be connected with criminal activities. Obviously, perpetrators of criminal acts strive to make the transactions as innocent-looking as possible. Because activities such as money laundering may involve complex organizational schemes, machine learning techniques based on individual transactions analysis may perform poorly when applied to suspicious transaction detection. In this paper, we propose a new machine learning method for mining transaction graphs. The method proposed in this paper builds a model of subgraphs that may contain suspicious transactions. The model used in our method is parametrized using fuzzy numbers which represent parameters of transactions and of the transaction subgraphs to be detected. Because money laundering may involve transferring money through a variable number of accounts the model representing transaction subgraphs is also parametrized with respect to some structural features. In contrast to some other graph mining methods in which graph isomorphisms are used to match data to the model, in our method we perform a fuzzy matching of graph structures. This paper presents a new approach for face recognition based on the fusion of tensors of census transform histograms from Local Gaussian features maps. Local Gaussian feature maps encode the most relevant information from Gaussian derivative features. Census Transform (CT) histograms are calculated and concatenated to form a tensor for each class of Gaussian map. Multi-linear Principal Component Analysis (MPCA) is applied to each tensor to reduce the number of dimensions as well as the correlation between neighboring pixels due to the Census Transform. We then train Kernel Discriminative Common Vectors (KDCV) to generate a discriminative vector using the results of the MPCA. Results of recognition using MPCA of tensors-CT histograms from Gaussian features maps with KDCV is shown to compare favorably with competing techniques that use more complex features maps like for example Gabor features maps in the FERET and Yale datasets. Additional experiments were done in the Yale B+ extended Yale B Faces dataset to show the performance of Gaussian features map with hard illumination changes. In this paper, we address the problems of contour detection, bottom-up grouping, object detection and semantic segmentation on RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset (, ECCV, 2012). We propose algorithms for object boundary detection and hierarchical segmentation that generalize the @math gPb-ucm approach of (TPAMI, 2011) by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We train RGB-D object detectors by analyzing and computing histogram of oriented gradients on the depth image and using them with deformable part models (, TPAMI, 2010). We observe that this simple strategy for training object detectors significantly outperforms more complicated models in the literature. We then turn to the problem of semantic segmentation for which we propose an approach that classifies superpixels into the dominant object categories in the NYUD2 dataset. We design generic and class-specific features to encode the appearance and geometry of objects. We also show that additional features computed from RGB-D object detectors and scene classifiers further improves semantic segmentation accuracy. In all of these tasks, we report significant improvements over the state-of-the-art. Probability distributions and histograms are natural representations for product ratings, traffic measurements, and other data considered in many machine learning applications. Thus, this paper introduces a technique for graph-based semi-supervised learning of histograms, derived from the theory of optimal transportation. Our method has several properties making it suitable for this application; in particular, its behavior can be characterized by the moments and shapes of the histograms at the labeled nodes. In addition, it can be used for histograms on non-standard domains like circles, revealing a strategy for manifold-valued semi-supervised learning. We also extend this technique to related problems such as smoothing distributions on graph nodes. Object detection has seen huge progress in recent years, much thanks to the heavily-engineered Histograms of Oriented Gradients (HOG) features. Can we go beyond gradients and do better than HOG? We provide an affirmative answer by proposing and investigating a sparse representation for object detection, Histograms of Sparse Codes (HSC). We compute sparse codes with dictionaries learned from data using K-SVD, and aggregate per-pixel sparse codes to form local histograms. We intentionally keep true to the sliding window framework (with mixtures and parts) and only change the underlying features. To keep training (and testing) efficient, we apply dimension reduction by computing SVD on learned models, and adopt supervised training where latent positions of roots and parts are given externally e.g. from a HOG-based detector. By learning and using local representations that are much more expressive than gradients, we demonstrate large improvements over the state of the art on the PASCAL benchmark for both root-only and part-based models. In this paper, a robust approach using a shape distribution histogram (SDH) feature and modified sparse representation classification (MSRC) for pedestrian detection in thermal infrared imagery is proposed. In this framework, the candidate regions that are more likely to contain the pedestrians are first detected based on the Contour Saliency Map. Then distances between random points on the thinned contour map of objects in the candidate regions are applied to acquire the SDH feature. SDH is a robust and discriminative feature which can precisely describe the pedestrian characteristics. Finally, a robust MSRC classifier which has high accuracy is used to recognize the true pedestrians. Experiments are conducted over the OSU thermal pedestrian database by comparing with other algorithms. The proposed method shows an excellent performance in detecting pedestrians. HighlightsRobust pedestrian detection using the SDH feature and MSRC classifier in infrared imagery.Discriminative SDH feature can precisely describe the pedestrian characteristic.Robust MSRC classifier is used to classify the pedestrians and non-pedestrians. In this paper, we propose a novel framework for contour based object detection. Compared to previous work, our contribution is three-fold. 1) A novel shape matching scheme suitable for partial matching of edge fragments. The shape descriptor has the same geometric units as shape context but our shape representation is not histogram based. 2) Grouping of partial matching hypotheses to object detection hypotheses is expressed as maximum clique inference on a weighted graph. 3) A novel local affine-transformation to utilize the holistic shape information for scoring and ranking the shape similarity hypotheses. Consequently, each detection result not only identifies the location of the target object in the image, but also provides a precise location of its contours, since we transform a complete model contour to the image. Very competitive results on ETHZ dataset, obtained in a pure shape-based framework, demonstrate that our method achieves not only accurate object detection but also precise contour localization on cluttered background. Local binary patterns of more bits extracted in a large structure have shown promising results in visual recognition applications. This results in very high-dimensional data so that it is not feasible to directly extract features from the LBP histogram, especially for a large-scale database. Instead of extracting features from the LBP histogram, we propose a new approach to learn discriminative LBP structures for a specific application. Our objective is to select an optimal subset of binarized-pixel-difference features to compose the LBP structure. As these features are strongly correlated, conventional feature-selection methods may not yield a desirable performance. Thus, we propose an incremental Maximal-Conditional-Mutual-Information scheme for LBP structure learning. The proposed approach has demonstrated a superior performance over the state-of-the-arts results on classifying both spatial patterns such as texture classification, scene recognition and face recognition, and spatial-temporal patterns such as dynamic texture recognition. HighlightsWe propose a new approach to tackle high-dimensional LBP features.It discovers optimal LBP structure to generate discriminative features.We propose a MCMI scheme for LBP structure learning to handle pixel correlation.It demonstrates a superior performance to SOTA on various visual applications. We present a discriminative Hough transform based object detector where each local part casts a weighted vote for the possible locations of the object center. We show that the weights can be learned in a max-margin framework which directly optimizes the classification performance. The discriminative training takes into account both the codebook appearance and the spatial distribution of its position with respect to the object center to derive its importance. On various datasets we show that the discriminative training improves the Hough detector. Combined with a verification step using a SVM based classifier, our approach achieves a detection rate of 91.9 at 0.3 false positives per image on the ETHZ shape dataset, a significant improvement over the state of the art, while running the verification step on at least an order of magnitude fewer windows than in a sliding window approach. Due to the high uptake of location-based services (LBSs), large spatio-temporal datasets of moving objects' trajectories are being created every day. An important task in spatial data analytics is to service range queries by returning trajectory counts within a queried region. The question of how to keep an individual user's data private whilst enabling spatial data analytics by third parties has become an urgent research direction. Indeed, it is increasingly becoming a concern for users. To preserve privacy we discard individual trajectories and aggregate counts over a spatial and temporal partition. However the privacy gained comes at a cost to utility: trajectories passing through multiple cells and re-entering a query region, lead to inaccurate query responses. This is known as the distinct counting problem. We propose the Connection Aware Spatial Euler (CASE) histogram to address this long-standing problem. The CASE histogram maintains the connectivity of a moving object path, but does not require the ID of an object to distinguish multiple entries into an arbitrary query region. Our approach is to process trajectories offline into aggregate counts which are sent to third parties, rather than the original trajectories. We also explore modifications of our aggregate counting approach that preserve differential privacy. Theoretically and experimentally we demonstrate that our method provides a high level of accuracy compared to the best known methods for the distinct counting problem, whilst preserving privacy. We conduct our experiments on both synthetic and real datasets over two competitive Euler histogram-based methods presented in the literature. Our methods enjoy improvements to accuracy from 10 up to 70 depending on trip data and query region size, with the greatest increase seen on the Microsoft T-Drive real dataset, representing a more than tripling of accuracy. A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate The detection of correlations between different features in a set of feature vectors is a very important data mining task because correlation indicates a dependency between the features or some association of cause and effect between them. This association can be arbitrarily complex, i.e. one or more features might be dependent from a combination of several other features. Well-known methods like the principal components analysis (PCA) can perfectly find correlations which are global, linear, not hidden in a set of noise vectors, and uniform, i.e. the same type of correlation is exhibited in all feature vectors. In many applications such as medical diagnosis, molecular biology, time sequences, or electronic commerce, however, correlations are not global since the dependency between features can be different in different subgroups of the set. In this paper, we propose a method called 4C (Computing Correlation Connected Clusters) to identify local subgroups of the data objects sharing a uniform but arbitrarily complex correlation. Our algorithm is based on a combination of PCA and density-based clustering (DBSCAN). Our method has a determinate result and is robust against noise. A broad comparative evaluation demonstrates the superior performance of 4C over competing methods such as DBSCAN, CLIQUE and ORCLUS. Studying trajectories of individuals has received growing interest. The aggregated movement behaviour of people provides important insights about their habits, interests, and lifestyles. Understanding and utilizing trajectory data is a crucial part of many applications such as location based services, urban planning, and traffic monitoring systems. Spatial histograms and spatial range queries are key components in such applications to efficiently store and answer queries on trajectory data. A spatial histogram maintains the sequentiality of location points in a trajectory by a strong sequential dependency among histogram cells. This dependency is an essential property in answering spatial range queries. However, the trajectories of individuals are unique and even aggregating them in spatial histograms cannot completely ensure an individual's privacy. A key technique to ensure privacy for data publishing ϵ-differential privacy as it provides a strong guarantee on an individual's provided data. Our work is the first that guarantees ϵ-differential privacy for spatial histograms on trajectories, while ensuring the sequentiality of trajectory data, i.e., its consistency. Consistency is key for any database and our proposed mechanism, PriSH, synthesizes a spatial histogram and ensures the consistency of published histogram with respect to the strong dependency constraint. In extensive experiments on real and synthetic datasets, we show that (1) PriSH is highly scalable with the dataset size and granularity of the space decomposition, (2) the distribution of aggregate trajectory information in the synthesized histogram accurately preserves the distribution of original histogram, and (3) the output has high accuracy in answering arbitrary spatial range queries.