This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

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Distinctive Image Features from Scale-Invariant Keypoints

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.

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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting k-means with a very simple, randomized seeding technique, we obtain an algorithm that is Θ(logk)-competitive with the optimal clustering. Preliminary experiments show that our augmentation improves both the speed and the accuracy of k-means, often quite dramatically.

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k-means++: the advantages of careful seeding

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.

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

We show how computation of geometric properties of a region represented by a linear quadtree can be speeded up by about a factor of p by using a p -processor CREW PRAM model of parallel computation. Similar speedups are obtained for computing the union and intersection of two regions, and the complement of a region, using linear quadtree representations.

Parallel processing of regions represented by linear quadtrees

Blum's medial axis transformation (MAT) for binary pictures yields medial axis points that lie midway between opposite borders of a region or along angle bisectors. This note discusses a generalization of the MAT in which a score is computed for each point P of a grayscale picture based on the gradient magnitudes at pairs of points that have P as their midpoint. These scores are high at points that lie midway between pairs of antiparallel edges or along angle bisectors, so that they define a MAT-like ``skeleton,'' which we may call the GRADMAT. However, this skeleton is rather sensitive to the presence of noise edges or to irregularities in the region edges, and it also is subject to artifacts created by pairs of edges belonging to different objects.

A Medial Axis Transformation for Grayscale Pictures

Geodesic visibility in graphs

Clustering is an important problem, with applications in areas such as data mining and knowledge discovery [6], data compression and vector quantiation [8], and pattern recognition and pattern classification [5]. One important class of clustering problems is given a set of n data points in real d-dimensional space, R, and an integer k. The problem is to determine a set of k points R, called centers, that minimizes the mean squared distance from each data point to its nearest center. This problem is closely related to other clustering problems, such as the k-medians problem [2], in which the objective is to minimize the sum of distances, and the k-center problem [1], in which the objective is to minimize the maximum distance. The results of Arora, Raghavan and Rao [2] can be extended to this problem, implying the existence of a polynomial time approximation scheme, but this algorithm is quite complex. Also see Hochbaum [9] and Jain and Dubes [11] for a more general description of clustering problems. One of the most popular heuristics for computing centers for minimizing the squared-error distortion is called the k-means algorithm [12, 7]. It is a simple iterative algorithm, and it may be used either for computing an initial set of centers or for producing a local improvement to a given set of centers. Here is how it works. Given any set of k centers, for each center ci, let Ri denote the set of data points for which ci

Computing nearest neighbors for moving points and applications to clustering

Given a binary image stored in a cellular array, a local reconfiguration process can be used to reconnect some of the cells into a quadtree network representing the image. This quadtree can also be ``roped,'' i.e., nodes representing adjacent image blocks of the same size can be joined. Using the roped quadtree network as a parallel (cellular) computer, image properties such as perimeter and genus, as well as the quadtree distance transform, can be computed in O(tree height) = O(log image diameter) time. The area and centroid of the image can be computed in O(height) time without the need for roping.

Angela Y. Wu

Papers

Parallel processing of regions represented by linear quadtrees

A Medial Axis Transformation for Grayscale Pictures

Geodesic visibility in graphs

Computing nearest neighbors for moving points and applications to clustering

Parallel Region Property Computation by Active Quadtree Networks