Elasticsearch (ES) is a distributed search and analytics engine consisting of a cluster of nodes, each hosting a disjoint subset of data. ES has a shared-nothing architecture that relies on local disks to store cluster data such as index files, transaction logs, and cluster state metadata. This stateful architecture couples compute with storage, and leads to different data tiers (e.g., hot, warm, cold, frozen) of hardware and configurations that the administrator chooses from to balance cost, performance, and high availability. In this paper, we show a new serverless architecture that decouples compute from storage. Serverless ES offloads data to an affordable, highly available cloud object store, while supporting the same APIs and read-after-write semantics. We show why and how this stateless architecture simplifies the tiers to just two: indexing and search, allowing indexing and searching practically limitless data while scaling each tier independently. We describe how we wrap index data in a custom batch commit format to the object store to decrease upload costs by up to 100x, how we batch transaction log uploads to decrease upload costs by up to 30x, and how we delete files from the object store. We experimentally show that Serverless ES can get twice the indexing throughput of (stateful) ES on comparable compute hardware by using object storage for durability instead of replication, and can scale linearly to match ingestion workloads.

Kernel classifiers based on Support Vector Machines (SVM) have recently achieved state-of-the art results on several popular datasets like Caltech or Pascal. This was possible by combining the advantages of SVM - convexity and the availability of efficient optimizers, with 'hyperkernels' - linear combinations of kernels computed at multiple levels of image encoding. The use of hyperkernels faces the challenge of choosing the kernel weights, the use of possibly irrelevant, poorly performing kernels, and an increased number of parameters that can lead to overfitting. In this paper we advocate the transition from SVMs to Support Kernel Machines (SKM) - models that estimate both the parameters of a sparse linear combination of kernels, and the parameters of a discriminative classifier. We exploit recent kernel learning techniques, not previously used in computer vision, that show how learning SKMs can be formulated as a convex optimization problem, which can be solved efficiently using Sequential Minimal Optimization. We study kernel learning for several multi-level image encodings for supervised object recognition and report competitive results on several datasets, including INRIA pedestrian, Caltech 101 and the newly created Caltech 256.

The wide availability of GPS sensors is changing the landscape in the applications of structure from motion techniques for localization. In this paper, we study the problem of estimating camera orientations from multiple views, given the positions of the viewpoints in a world coordinate system and a set of point correspondences across the views. Given three or more views, the above problem has a finite number of solutions for three or more point correspondences. Given six or more views, the problem has a finite number of solutions for just two or more points. In the three-view case, we show the necessary and sufficient conditions for the three essential matrices to be consistent with a set of known baselines. We also introduce a method to recover the absolute orientations of three views in world coordinates from their essential matrices. To refine these estimates we perform a least-squares minimization on the group cross product SO(3) × SO(3) × SO(3). We report experiments on synthetic data and on data from the ICCV2005 Computer Vision Contest.