Topic
Locality-sensitive hashing
About: Locality-sensitive hashing is a research topic. Over the lifetime, 1894 publications have been published within this topic receiving 69362 citations.
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TL;DR: A high throughput ANN-searching processor is proposed for high-resolution (full-HD) and real-time (30 fps) video object recognition and adopts an interframe cache architecture as a hardware-oriented approach and a zeroless locality-sensitive-hashing (zeroless-LSH) algorithm as a software- oriented approach to reduce the external memory bandwidth required in nearest neighbor searching.
Abstract: Approximate nearest neighbor (ANN) searching is an essential task in object recognition. The ANN-searching stage, however, is the main bottleneck in the object recognition process due to increasing database size and massive dimensions of keypoint descriptors. In this paper, a high throughput ANN-searching processor is proposed for high-resolution (full-HD) and real-time (30 fps) video object recognition. The proposed ANN-searching processor adopts an interframe cache architecture as a hardware-oriented approach and a zeroless locality-sensitive-hashing (zeroless-LSH) algorithm as a software-oriented approach to reduce the external memory bandwidth required in nearest neighbor searching. A four-way set associative on-chip cache has a dedicated architecture to exploit data correlation at the frame-level. Zeroless-LSH minimizes data transactions from external memory at the vector-level. The proposed ANN-searching processor is fabricated as part of an object recognition SoC using a 0.13 μm 6 metal CMOS technology. It achieves 62 720 vectors/s throughput and 1140 GOPS/W power efficiency, which are 1.45 and 1.37 times higher than the state-of-the-art, respectively, enabling real-time object recognition for full-HD 30 fps video streams.
14 citations
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03 Jan 201814 citations
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TL;DR: A flexible and fast distributed video deduplication framework based on hash codes that is able to support the hash table indexing using any existing hashing algorithm in a distributed environment and can efficiently rank the candidate videos by exploring the similarities among the key frames over multiple tables using MapReduce strategy.
Abstract: The exponentially growing amount of video data being produced has led to tremendous challenges for video deduplication technology. Nowadays, many different deduplication approaches are being rapidly developed, but they are generally slow and their identification processes are somewhat inaccurate. Till now, there is rare work that studies the generic hash-based distributed framework and the efficient similarity ranking strategy for video deduplication. This paper proposes a flexible and fast distributed video deduplication framework based on hash codes. It is able to support the hash table indexing using any existing hashing algorithm in a distributed environment and can efficiently rank the candidate videos by exploring the similarities among the key frames over multiple tables using MapReduce strategy. Our experiments with a popular large-scale dataset demonstrate that the proposed framework can achieve satisfactory video deduplication performance.
14 citations
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14 citations
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06 Nov 2006TL;DR: This paper proposes an approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time by using an approximation of the nearest-neighbor-distances in order to prune the search space.
Abstract: In this paper, we propose an approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. Our method uses an approximation of the nearest-neighbor-distances in order to prune the search space. In several experiments, our solution scales significantly better than existing non-approximative approaches while producing an approximation of the true query result with a high recall.
14 citations