This paper examines how computation can be mapped across the nodes of a distributed search system to effectively utilize available resources. We specifically address computationally intensive search of complex data, such as content-based retrieval of digital images or sounds, where sophisticated algorithms must be evaluated on the objects of interest. Since these problems require significant computation, we distribute the search over a collection of compute nodes, such as active storage devices, intermediate processors and host computers. A key challenge with mapping the desired computation to the available resources is that the most efficient distribution depends on several factors: relative power and number of compute nodes; network bandwidth between the compute nodes; the cost of evaluating query predicates; and the selectivity of the given query. This wide range of variables renders manual partitioning of the computation infeasible, particularly since some of the parameters (e.g., available network bandwidth) can change during the course of a search. This paper proposes several techniques for dynamic partitioning of computation, and demonstrates that they can significantly improve efficiency for distributed search applications.

This paper evaluates the effectiveness of keypoint methods for content-based protection of digital images. These methods identify a set of "distinctive" regions (termed keypoints) in an image and encode them using descriptors that are robust to expected image transformations. To determine whether particular images were derived from a protected image, the keypoints for both images are generated and their descriptors matched. We describe a comprehensive set of experiments to examine how keypoint methods cope with three real-world challenges: (1) loss of keypoints due to cropping; (2) matching failures caused by approximate nearest-neighbor indexing schemes; (3) degraded descriptors due to significant image distortions. While keypoint methods perform very well in general, this paper identifies cases where the accuracy of such methods degrades.

SnapFind is an image retrieval system that enables efficient interactive search of large data sets by exploiting active disk technology. In contrast to earlier approaches, where data is typically pre-indexed for efficient retrieval according to a fixed scheme, SnapFind provides users with the flexibility to search non-indexed data in a brute force manner. The query is translated into a customized searchlet that is executed in parallel by processors near the storage devices. This enables the majority of irrelevant images to be discarded where they are stored. Partial results are displayed during search execution allowing users to interactively refine the query without waiting for search termination. This paper argues that algorithms with user-adjustable parameters are preferable to black-box image retrieval techniques.

We introduce a system for near-duplicate detection and sub-image retrieval. Such a system is useful for finding copyright violations and detecting forged images. We define near-duplicate as images altered with common transformations such as changing contrast, saturation, scaling, cropping, framing, etc. Our system builds a parts-based representation of images using distinctive local descriptors which give high quality matches even under severe transformations. To cope with the large number of features extracted from the images, we employ locality-sensitive hashing to index the local descriptors. This allows us to make approximate similarity queries that only examine a small fraction of the database. Although locality-sensitive hashing has excellent theoretical performance properties, a standard implementation would still be unacceptably slow for this application. We show that, by optimizing layout and access to the index data on disk, we can efficiently query indices containing millions of keypoints. Our system achieves near-perfect accuracy (100% precision at 99.85% recall) on the tests presented in Meng et al. [16], and consistently strong results on our own, significantly more challenging experiments. Query times are interactive even for collections of thousands of images.