This paper proposes a fast and scalable alternating optimization technique to detect regions of interest (ROIs) in cluttered Web images without labels. The proposed approach discovers highly probable regions of object instances by iteratively repeating the following two functions: (1) choose the exemplar set (i.e. a small number of highly ranked reference ROIs) across the dataset and (2) refine the ROIs of each image with respect to the exemplar set. These two subproblems are formulated as ranking in two different similarity networks of ROI hypotheses by link analysis. The experiments with the PASCAL 06 dataset show that our unsupervised localization performance is better than one of state-of-the-art techniques and comparable to supervised methods. Also, we test the scalability of our approach with five objects in Flickr dataset consisting of more than 200K images. 1

In this paper we present an efficient technique to obtain accurate semantic classification on the pixel level capable of integrating various modalities, such as color, edge responses, and height information. We propose a novel feature representation based on Sigma Points computations that enables a simple application of powerful covariance descriptors to a multi-class randomized forest framework. Additionally, we include semantic contextual knowledge using a conditional random field formulation. In order to achieve a fair comparison to state-of-the-art methods our approach is first evaluated on the MSRC image collection and is then demonstrated on three challenging aerial image datasets Dallas, Graz, and San Francisco. We obtain a full semantic classification on single aerial images within two minutes. Moreover, the computation time on large scale imagery including hundreds of images is investigated.

Common visual codebook generation methods used in a Bag of Visual words model, e.g. k-means or Gaussian Mixture Model, use the Euclidean distance to cluster features into visual code words. However, most popular visual descriptors are histograms of image measurements. It has been shown that the Histogram Intersection Kernel (HIK) is more effective than the Euclidean distance in supervised learning tasks with histogram features. In this paper, we demonstrate that HIK can also be used in an unsupervised manner to significantly improve the generation of visual codebooks. We propose a histogram kernel k-means algorithm which is easy to implement and runs almost as fast as k-means. The HIK codebook has consistently higher recognition accuracy over k-means codebooks by 2-4%. In addition, we propose a one-class SVM formulation to create more effective visual code words which can achieve even higher accuracy. The proposed method has established new state-of-the-art performance numbers for 3 popular benchmark datasets on object and scene recognition. In addition, we show that the standard k-median clustering method can be used for visual codebook generation and can act as a compromise between HIK and k-means approaches. 1.

In this paper, we study the problem of fine-grained image categorization. The goal of our method is to explore fine image statistics and identify the discriminative image patches for recognition. We achieve this goal by combining two ideas, discriminative feature mining and randomization. Discriminative feature mining allows us to model the detailed information that distinguishes different classes of images, while randomization allows us to handle the huge feature space and prevents over-fitting. We propose a random forest with discriminative decision trees algorithm, where every tree node is a discriminative classifier that is trained by combining the information in this node as well as all upstream nodes. Our method is tested on both subordinate categorization and activity recognition datasets. Experimental results show that our method identifies semantically meaningful visual information and outperforms stateof-the-art algorithms on various datasets. 1.

Recent approaches to multi-view learning have shown that factorizing the information into parts that are shared across all views and parts that are private to each view could effectively account for the dependencies and independencies between the different input modalities. Unfortunately, these approaches involve minimizing non-convex objective functions. In this paper, we propose an approach to learning such factorized representations inspired by sparse coding techniques. In particular, we show that structured sparsity allows us to address the multiview learning problem by alternately solving two convex optimization problems. Furthermore, the resulting factorized latent spaces generalize over existing approaches in that they allow having latent dimensions shared between any subset of the views instead of between all the views only. We show that our approach outperforms state-of-the-art methods on the task of human pose estimation. 1

Abstract. In this paper, we propose a method for extracting image features which utilizes 2 nd order statistics, i.e., spatial and orientational auto-correlations of local gradients. It enables us to extract richer information from images and to obtain more discriminative power than standard histogram based methods. The image gradients are sparsely described in terms of magnitude and orientation. In addition, normal vectors on the image surface are derived from the gradients and these could also be utilized instead of the gradients. From a geometrical viewpoint, the method extracts information about not only the gradients but also the curvatures of the image surface. Experimental results for pedestrian detection and image patch matching demonstrate the effectiveness of the proposed method compared with other methods, such as HOG and SIFT. 1

Random Forests (RFs) have become commonplace in many computer vision applications. Their popularity is mainly driven by their high computational efficiency during both training and evaluation while still being able to achieve state-of-the-art accuracy. This work extends the usage of Random Forests to Semi-Supervised Learning (SSL) problems. We show that traditional decision trees are optimizing multiclass margin maximizing loss functions. From this intuition, we develop a novel multi-class margin definition for the unlabeled data, and an iterative deterministic annealing-style training algorithm maximizing both the multi-class margin of labeled and unlabeled samples. In particular, this allows us to use the predicted labels of the unlabeled data as additional optimization variables. Furthermore, we propose a control mechanism based on the out-of-bag error, which prevents the algorithm from degradation if the unlabeled data is not useful for the task. Our experiments demonstrate state-of-the-art semisupervised learning performance in typical machine learning problems and constant improvement using unlabeled data for the Caltech-101 object categorization task. 1.

Abstract. The application of semi-supervised learning algorithms to large scale vision problems suffers from the bad scaling behavior of most methods. Based on the Expectation Regularization principle, in this paper we propose a novel semi-supervised boosting method, called SERBoost that can be applied to large scale vision problems and its complexity is dominated by the base learners. The algorithm provides a margin regularizer for the boosting cost function and shows a principled way of utilizing prior knowledge. We demonstrate the performance of SERBoost on the Pascal VOC2006 set and compare it to other supervised and semisupervised methods, where SERBoost shows improvements both in terms of classification accuracy and computational speed. 1

The selection of weak classifiers is critical to the success of boosting techniques. Poor weak classifiers do not perform better than random guess, thus cannot help decrease the training error during the boosting process. Therefore, when constructing the weak classifier pool, we prefer the quality rather than the quantity of the weak classifiers. In this paper, we present a data mining-driven approach to discovering compositional features from a given and possibly small feature pool. Compared with individual features (e.g. weak decision stumps) which are of limited discriminative ability, the mined compositional features have guaranteed power in terms of the descriptive and discriminative abilities, as well as bounded training error. To cope with the combinatorial cost of discovering compositional features, we apply data mining methods (frequent itemset mining) to efficiently find qualified compositional features of any possible order. These weak classifiers are further combined through a multi-class AdaBoost method for final multi-class classification. Experiments on a challenging 10-class event recognition problem show that boosting compositional features can lead to faster decrease of training error and significantly higher accuracy compared to conventional boosting decision stumps. 1.

This chapter deals with the problem of whole-image categorization. We may want to classify a photograph based on a high-level semantic attribute (e.g., indoor or outdoor), scene type (forest, street, office, etc.), or object category (car, face, etc.). Our philosophy is that such global image tasks can be approached in a holistic fashion: It should be possible to develop image representations that use low-level features to directly infer high-level semantic information about the scene without going through the intermediate step of segmenting the image into more “basic” semantic entities. For example, we should be able to recognize that an image contains a beach scene without first segmenting and identifying its separate components, such as sand, water, sky, or bathers. This philosophy is inspired by psychophysical and psychological evidence that people can recognize scenes by considering them in a “holistic ” manner, while overlooking most of the details of the constituent objects (Oliva and Torralba, 2001). It has been shown that human subjects can perform high-level categorization tasks extremely rapidly