Data Mining - Concepts and Techniques.

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

/pdf/anomaly-detection-a-survey-1x33g9m0a3.pdf

Anomaly detection: A survey

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.

Shape matching and object recognition using shape contexts

Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

/pdf/content-based-image-retrieval-at-the-end-of-the-early-years-q45iy9ejai.pdf

Content-based image retrieval at the end of the early years

In this paper, the authors argue that shape representation and recovery can be accomplished using digital non-parametric models and methods. First, we describe some recent results in digital topology and digital geometry. It is shown, for example, that for compact, twice-differentiable objects, which we refer to as parallel regular, a digitization resolution always exists that preserves topology arid that a topology-preserving digitization resolution r always preserves the qualitative differential geometry of the object surface. Moreover, it is shown that if an object is parallel regular, very few digital boundary patterns are realizable and that each such digital neighborhood has a well-defined geometric interpretation with respect to tangent direction. We then define the set of digital boundary curves as either an adjacency graph or a grammar, and show how to recursively enumerate the set of such curves. Finally, we describe how a generalized torus can be recovered from a single intensity image using digital non-parametric models and methods.

Toward Non-Parametric Digital Shape Representation and Recovery

In this paper we discuss the resilience of moving objects detection algorithm based on spatiotemporal blocks on VQriOtLY types of ditive and multiplicative noise. After a given video is decomposed into the spariotemporal blockr, the algorithm uses dimensiondity reduction technique to obtain a compact vector representation of each block and to suppress the injluence of noise. We evaluate the algorithm performance by comparing ''pound truth" (hand-labeled moving objects) to properly de$ned spatial-windows based evaluation statistics. Our results on a PETS repository video show that detection and tracking of moving objects is substantially improved in presence of Gawsim, speckle, multiplicative and Poisson noise.

https://cis.temple.edu/~latecki/Papers/PokrajacZeljkovicLateckiELMAR2005.pdf

Noise-resilient detection of moving objects based on spatial-temporal blocks

We present a force field based approach for simultaneous alignment of multiple laser scans in robot mapping. It avoids sensitive behavior to wrong data associations and sparse sensing, which are the main challenges e.g. in multi r obot mapping under the constraints given in autonomous search an d rescue robotics. The presented algorithm solves the alignm ent problem utilizing a gradient descent approach motivated by physics, but exchanges laws of physics with constraints giv en by human perception. Experiments on different real world data sets show the successful application of the algorithm.

/pdf/force-field-based-n-scan-alignment-1f4467klsp.pdf

Force Field Based n-Scan Alignment.

Our definition of a digitization approximates many real digitization processes. We present conditions which guarantee that a digitization process preserves topology of a digitized object. Moreover, these conditions guarantee the invariance of convexity features of the object contour. A useful consequence of this result is the computation of a digitization resolution and a camera distance from a planar object such that topology and geometry of an object are preserved under projection and digitization. Knowing that topological properties are invariant under digitization, we can then use them in feature-based recognition.

Digitization constraints that preserve topology and geometry

Starting with the intuitive concept of `nearness' as a binary relation, semi-proximity spaces (sp-spaces) are defined. The restrictions on semi-proximity spaces are weaker than the restrictions on topological proximity spaces. Nevertheless, semi-proximity spaces generalize classical topological spaces. Moreover, it is possible to describe all digital pictures used in computer vision and computer graphics as non-trivial semi-proximity spaces, which is not possible in classical topology. Therefore, we use semi-proximity spaces to establish a formal relationship between the `topological' concepts of digital image processing and their continuous counterparts in Rn. Especially interesting are continuous functions in semi- proximity spaces. The definition of a `nearly' bicontinuous function is given which does not require the function to be one-to-one. A nearly bicontinuous function preserves connectedness in both directions. Therefore, nearly bicontinuous functions can be used for characterizing well-behaved operations on digital images such as thinning. Further, it is shown that the deletion of a simple point can be treated as a nearly bicontinuous function. These properties and the fact that a variety of nearness relations can be defined on digital pictures indicate that nearly continuous functions are a useful tool in the difficult task of shape description.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Nearly continuous functions in digital images

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