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

http://library.utia.cas.cz/prace/20030125.pdf

Image registration methods: a survey

While automatic image registration algorithms are usually being evaluated with regards to their accuracy, it is often useful to relate this accuracy to the "initial conditions", i.e., the distance between the initial navigation geolocation and the correct result. This paper describes a modular framework that was built to describe registration algorithms, and utilize this framework to attempt to classify different registration components and algorithms in terms of their responses to the initial conditions. Performances would be evaluated on synthetic data, multitemporal and multisensor data. All results of the study would be presented at the conference and would be useful for two different purposes: (1) provide automatic quality assessment of the geolocation of remote sensing data by performing interalgorithm consistency studies; and (2) be the foundations for the design of future on-board applications including planetary exploration.

A study of the sensitivity of automatic image registration algorithms to initial conditions

Ethnic group classification is a well-researched problem, which has been pursued mainly during the past two decades via traditional approaches of image processing and machine learning. In this paper, we propose a method of classifying an image face into an ethnic group by applying transfer learning from a previously trained classification network for large-scale data recognition. Our proposed method yields state-of-the-art success rates of 99.02%, 99.76%, 99.2%, and 96.7%, respectively, for the four ethnic groups: African, Asian, Caucasian, and Indian.

DeepEthnic: Multi-Label Ethnic Classification from Face Images

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible.

DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

Image registration (IR) is a fundamental task in image processing for matching two or more images of the same scene taken at different times, from different viewpoints and/or by different sensors. Due to the enormous diversity of IR applications, automatic IR remains a challenging problem to this day. A wide range of techniques has been developed for various data types and problems. These techniques might not handle effectively very large images, which give rise usually to more complex transformations, e.g., deformations and various other distortions. In this paper we present a genetic programming (GP)- based approach for IR, which could offer a significant advantage in dealing with very large images, as it does not make any prior assumptions about the transformation model. Thus, by incorporating certain generic building blocks into the proposed GP framework, we hope to realize a large set of specialized transformations that should yield accurate registration of very large images.

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Image Registration of Very Large Images via Genetic Programming

As the amount of multidimensional remotely sensed data is growing tremendously, Earth scientists need more efficient ways to search and analyze such data. In particular, extracting image content is emerging as one of the most powerful tools to perform data mining. One of the most promising methods to extract image content is image classification, which provides a labeling of each pixel in the image. In this paper, we concentrate on neural network classifiers and we show how information obtained with a wavelet transform can be integrated to such a classifier. We apply a local spatial frequency analysis, a wavelet transform, to account for statistical texture information in Landsat/TM imagery. Statistical texture is extracted with a continuous edge-texture composite wavelet transform. We show how this approach relates to texture information computed from a co-occurrence matrix. The network is then trained with both the texture information and the additional pixel labels provided by the ground truth data. Theory and preliminary results are described in this paper. In future work, this pre-processing will automatically generate a texture index which will be fed into the artificial neural network, thus providing better discriminants of Landsat/TM imagery.

Nathan S. Netanyahu

Papers

A study of the sensitivity of automatic image registration algorithms to initial conditions

DeepEthnic: Multi-Label Ethnic Classification from Face Images

DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

Image Registration of Very Large Images via Genetic Programming

Wavelet index of texture for artificial neural network classification of Landsat images