Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been tested on more than a handful of object categories. We present an method for learning object categories from just a few training images. It is quick and it uses prior information in a principled way. We test it on a dataset composed of images of objects belonging to 101 widely varied categories. Our proposed method is based on making use of prior information, assembled from (unrelated) object categories which were previously learnt. A generative probabilistic model is used, which represents the shape and appearance of a constellation of features belonging to the object. The parameters of the model are learnt incrementally in a Bayesian manner. Our incremental algorithm is compared experimentally to an earlier batch Bayesian algorithm, as well as to one based on maximum-likelihood. The incremental and batch versions have comparable classification performance on small training sets, but incremental learning is significantly faster, making real-time learning feasible. Both Bayesian methods outperform maximum likelihood on small training sets. I.

Learning visual models of object categories notoriously requires thousands of training examples; this is due to the diversity and richness of object appearance which requires models containing hundreds of parameters. We present a method for learning object categories from just a few images ( � �). It is based on incorporating “generic” knowledge which may be obtained from previously learnt models of unrelated categories. We operate in a variational Bayesian framework: object categories are represented by probabilistic models, and “prior ” knowledge is represented as a probability density function on the parameters of these models. The “posterior ” model for an object category is obtained by updating the prior in the light of one or more observations. Our ideas are demonstrated on four diverse categories (human faces, airplanes, motorcycles, spotted cats). Initially three categories are learnt from hundreds of training examples, and a “prior ” is estimated from these. Then the model of the fourth category is learnt from 1 to 5 training examples, and is used for detecting new exemplars a set of test images. 1.

Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by Maximum Likelihood (ML) and Maximum A Posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully.

A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported.

Face detection systems have recently achieved high detection rates[11, 8, 5] and real-time performance[11]. However, these methods usually rely on a huge training database (around 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 multiview face detection the performance of existing systems is far from satisfactory.

into a computing giant able to run resource-intensive applications such as natural language translation, speech recognition, face recognition, and augmented reality. However, easily partitioning these applications for remote execution while retaining application-specific information has proven to be a difficult challenge. In this paper, we show that automated dynamic repartitioning of mobile applications can be reconciled with the need to exploit application-specific knowledge. We show that the useful knowledge about an application relevant to remote execution can be captured in a compact declarative form called tactics. Tactics capture the full range of meaningful partitions of an application and are very small relative to code size. We present the design of a tactics-based remote execution system, Chroma, that performs comparably to a runtime system that makes perfect partitioning decisions. Furthermore, we show that Chroma can automatically use extra resources in an overprovisioned environment to improve application performance.

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Normally, the primary purpose of an information display is to convey information. If information displays can be aesthetically interesting, that might be an added bonus. This paper considers an experiment in reversing this imperative. It describes the Kandinsky system which is designed to create displays which are first aesthetically interesting, and then as an added bonus, able to convey information. The Kandinsky system works on the basis of aesthetic properties specified by an artist (in a visual form). It then explores a space of collages composed from information bearing images, using an optimization technique to find compositions which best maintain the properties of the artist’s aesthetic expression. Keywords Visual design, aesthetics in computational objects, display generation, ambient information displays in decorative objects, optimization, simulated annealing. “…But does it go with the couch?”

Abstract. We investigate a method for learning object categories in a weakly supervised manner. Given a set of images known to contain the target category from a similar viewpoint, learning is translation and scale-invariant; does not require alignment or correspondence between the training images, and is robust to clutter and occlusion. Category models are probabilistic constellations of parts, and their parameters are estimated by maximizing the likelihood of the training data. The appearance of the parts, as well as their mutual position, relative scale and probability of detection are explicitly described in the model. Recognition takes place in two stages. First, a featurefinder identifies promising locations for the model’s parts. Second, the category model is used to compare the likelihood that the observed features are generated by the category model, or are generated by background clutter. The flexible nature of the model is demonstrated by results over six diverse object categories including geometrically constrained categories (e.g. faces, cars) and flexible objects (such as animals).

Abstract. The problem of recognizing classes of objects as opposed to special instances requires methods of comparing images that capture the variation within the class while they discriminate against objects outside the class. We present a simple method for image description based on histograms of qualitative shape indexes computed from the combination of triplets of sampled locations and gradient directions in the image. We demonstrate that this method indeed is able to capture variation within classes of objects and we apply it to the problem of recognizing four different categories from a large database. Using our descriptor on the whole image, containing varying degrees of background clutter, we obtain results for two of the objects that are superior to the best results published so far for this database. By cropping images manually we demonstrate that our method has a potential to handle also the other objects when supplied with an algorithm for searching the image. We argue that our method, based on qualitative image properties, capture the large range of variation that is typically encountered within an object class. This means that our method can be used on substantially larger patches of images than existing methods based on simpler criteria for evaluating image similarity.