How do we identify images of the same person in photo albums? How can we find images of a particular celebrity using Web image search engines? These types of tasks require solving numerous challenging issues in computer vision including: detecting whether an image contains a face, maintaining robustness to lighting, pose, occlusion, scale, and image quality, and using appropriate distance metrics to identify and compare different faces. In this paper we present a complete system which yields good performance on challenging tasks involving face recognition including image retrieval, unsupervised clustering of faces, and increasing precision of dasiaGoogle Imagepsila searches. All tasks use highly variable real data obtained from raw image searches on the web.

Automatically determining facial similarity is a difficult and open question in computer vision. The problem is complicated both because it is unclear what facial features humans use to determine facial similarity and because facial similarity is subjective in nature: similarity judgements change from person to person. In this work we suggest a system which places facial similarity on a solid computational footing. First we describe methods for acquiring facial similarity ratings from humans in an efficient manner. Next we show how to create feature vector representations for each face by extracted patches around facial key-points. Finally we show how to use the acquired similarity ratings to learn functional mapping which project facial-feature vectors into face spaces which correspond to our notions of facial similarity. We use different collections of images to both create and validate the face spaces including: perceptual similarity data obtained from humans, morphed faces between two different individuals, and the CMU PIE collection which contains images of the same individual under different lighting conditions. We demonstrate that using our methods we can effectively create face spaces which correspond to human notions of facial similarity.

Probabilistic modelling of text data in the bag-of-words representation has been dominated by directed graphical models such as pLSI, LDA, NMF, and discrete PCA. Recently, state of the art performance on visual object recognition has also been reported using variants of these models. We introduce an alternative undirected graphical model suitable for modelling count data. This "Rate Adapting Poisson" (RAP) model is shown to generate superior dimensionally reduced representations for subsequent retrieval or classification. Models are trained using contrastive divergence while inference of latent topical representations is efficiently achieved through a simple matrix multiplication.

Here we explore a discriminative learning method on underlying generative models for the purpose of discriminating between object categories. Visual recognition algorithms learn models from a set of training examples. Generative models learn their representations by considering data from a single class. Generative models are popular in computer vision for many reasons, including their ability to elegantly incorporate prior knowledge and to handle correspondences between object parts and detected features. However, generative models are often inferior to discriminative models during classification tasks. We study a discriminative approach to learning object categories which maintains the representational power of generative learning, but trains the generative models in a discriminative manner. The discriminatively trained models perform better during classification tasks as a result of selecting discriminative sets of features. We conclude by proposing a multi-class object recognition system which initially trains object classes in a generative manner, identifies subsets of similar classes with high confusion, and finally trains models for these subsets in a discriminative manner to realize gains in classification performance.

Learning models for detecting and classifying object categories is a challenging problem in machine vision. While discriminative approaches to learning and classification have, in principle, superior performance, generative approaches provide many useful features, one of which is the ability to naturally establish explicit correspondence between model components and scene features - this, in turn, allows for the handling of missing data and unsupervised learning in clutter. We explore a hybrid generative/discriminative approach using 'Fisher kernels' by Jaakkola and Haussler (1999) which retains most of the desirable properties of generative methods, while increasing the classification performance through a discriminative setting. Furthermore, we demonstrate how this kernel framework can be used to combine different types of features and models into a single classifier. Our experiments, conducted on a number of popular benchmarks, show strong performance improvements over the corresponding generative approach and are competitive with the best results reported in the literature.

Re-mapping patterns in order to equalize their distribution may greatly simplify both the structure and the training of classifiers. Here, the properties of one such map obtained by running a few steps of discrete-time dynamical system are explored. The system is called 'Digital Antennal Lobe' (DAL) because it is inspired by recent studies of the antennallobe, a structure in the olfactory sys(cid:173) tem of the grasshopper. The pattern-spreading properties of the DAL as well as its average behavior as a function of its (few) de(cid:173) sign parameters are analyzed by extending previous results of Van Vreeswijk and Sompolinsky. Furthermore, a technique for adapting the parameters of the initial design in order to obtain opportune noise-rejection behavior is suggested. Our results are demonstrated with a number of simulations.