Fast and high-quality document clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In particular, hierarchical clustering solutions provide a view of the data at different levels of granularity, making them ideal for people to visualize and interactively explore large document collections.

Object recognition has reached a level where we can identify a large number of previously seen and known objects. However, the more challenging and important task of categorizing previously unseen objects remains largely unsolved. Traditionally, contour and shape based methods are regarded most adequate for handling the generalization requirements needed for this task. Appearance based methods, on the other hand, have been successful in object identification and detection scenarios. Today little work is done to systematically compare existing methods and characterize their relative capabilities for categorizing objects. In order to compare different methods we present a new database specifically tailored to the task of object categorization. It contains high-resolution color images of 80 objects from 8 different categories, for a total of 3280 images. It is used to analyze the performance of several appearance and contour based methods. The best categorization result is obtained by an appropriate combination of different methods.

Abstract. In this paper we present a novel class-based segmentation method, which is guided by a stored representation of the shape of objects within a general class (such as horse images). The approach is different from bottom-up segmentation methods that primarily use the continuity of grey-level, texture, and bounding contours. We show that the method leads to markedly improved segmentation results and can deal with significant variation in shape and varying backgrounds. We discuss the relative merits of class-specific and general image-based segmentation methods and suggest how they can be usefully combined. Keywords: Grouping and segmentation; Figure-ground; Top-down processing; Object classification

In recent years, we have witnessed a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intranets. This has led to an increased interest in developing methods that can help users to effectively navigate, summarize, and organize this information with the ultimate goal of helping them to find what they are looking for. Fast and high-quality document clustering algorithms play an important role towards this goal as they have been shown to provide both an intuitive navigation/browsing mechanism by organizing large amounts of information into a small number of meaningful clusters as well as to greatly improve the retrieval performance either via cluster-driven dimensionality reduction, term-weighting, or query expansion. This ever-increasing importance of document clustering and the expanded range of its applications led to the development of a number of new and novel algorithms with different complexity-quality trade-offs. Among them, a class of clustering algorithms that have relatively low computational requirements are those that treat the clustering problem as an optimization process which seeks to maximize or minimize a particular clustering criterion function defined over the entire clustering solution.

We present the design of the eXtreme Scale Mote, a new sensor network platform for reliably detecting and classifying, and quickly reporting, rare, random, and ephemeral events in a largescale, long-lived, and retaskable manner. This new mote was designed for the ExScal project which seeks to demonstrate a 10,000 node network capable of discriminating civilians, soldiers and vehicles, spread out over a 10km 2 area, with node lifetimes approaching 1,000 hours of continuous operation on two AA alkaline batteries. This application posed unique functional, usability, scalability, and robustness requirements which could not be met with existing hardware, and therefore motivated the design of a new platform. The detection and classification requirements are met using infrared, magnetic, and acoustic sensors. The infrared and acoustic sensors are designed for low-power continuous operation and include asynchronous processor wakeup circuitry. The usability and scalability requirements are met by minimizing the frequency and cost of human-in-the-loop operations during node deployment, activation, and verification through improvements in the user interface, packaging, and configurability of the platform. Recoverable retasking is addressed by using a grenade timer that periodically forces a system reset. The key contributions of this work are a specific design point and general design methods for building sensor network platforms to detect exceptional events. 1.

Supervised and unsupervised learning methods have traditionally focused on data consisting of independent instances of a single type. However, many real-world domains are best described by relational models in which instances of multiple types are related to each other in complex ways. For example, in a scientific paper domain, papers are related to each other via citation, and are also related to their authors. In this case, the label of one entity (e.g., the topic of the paper) is often correlated with the labels of related entities. We propose a general class of models for classification and clustering in relational domains that capture probabilistic dependencies between related instances. We show how to learn such models efficiently from data. We present empirical results on two real world data sets. Our experiments in a transductive classification setting indicate that accuracy can be significantly improved by modeling relational dependencies. Our algorithm automatically induces a very natural behavior, where our knowledge about one instance helps us classify related ones, which in turn help us classify others. In an unsupervised setting, our models produced coherent clusters with a very natural interpretation, even for instance types that do not have any attributes. 1

We present a method whereby a robot can learn to recognize places with high accuracy, in spite of perceptual aliasing (different places appear the same) and image variability (the same place appears differently). The first step in learning place recognition restricts attention to distinctive states identified by the map-learning algorithm, and eliminates image variability by unsupervised learning of clusters of similar sensory images. The clusters define views associated with distinctive states, often increasing perceptual aliasing. The second step eliminates perceptual aliasing by building a causal/topological map and using history information gathered during exploration to disambiguate distinctive states. The third step uses the labeled images for supervised learning of direct associations from sensory images to distinctive states. We evaluate the method using a physical mobile robot in two environments, showing high recognition rates in spite of large amounts of perceptual aliasing.

State-of-the-art image classification methods require an intensive learning/training stage (using SVM, Boosting, etc.) In contrast, non-parametric Nearest-Neighbor (NN) based image classifiers require no training time and have other favorable properties. However, the large performance gap between these two families of approaches rendered NNbased image classifiers useless. We claim that the effectiveness of non-parametric NNbased image classification has been considerably undervalued. We argue that two practices commonly used in image classification methods, have led to the inferior performance of NN-based image classifiers: (i) Quantization of local image descriptors (used to generate “bags-of-words”, codebooks). (ii) Computation of ‘Image-to-Image ’ distance, instead of ‘Image-to-Class ’ distance. We propose a trivial NN-based classifier – NBNN, (Naive-Bayes Nearest-Neighbor), which employs NNdistances in the space of the local image descriptors (and not in the space of images). NBNN computes direct ‘Imageto-Class’ distances without descriptor quantization. We further show that under the Naive-Bayes assumption, the theoretically optimal image classifier can be accurately approximated by NBNN. Although NBNN is extremely simple, efficient, and requires no learning/training phase, its performance ranks among the top leading learning-based image classifiers. Empirical comparisons are shown on several challenging databases (Caltech-101,Caltech-256 and Graz-01). 1.

Abstract—A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning. Index Terms—Content-based image retrieval, semantic image annotation and retrieval, weakly supervised learning, multiple instance learning, Gaussian mixtures, expectation-maximization, image segmentation, object recognition. 1

We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.