Publication
ICMEW 2014
Conference paper

Concurrent image query using local random walk with restart on large scale graphs

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Abstract

Efficient image query is a fundamental challenge in many large scale multimedia applications, especially when handling many queries concurrently. In this paper, we proposed a novel approach called graph local random walk for high performance concurrent image query. Specifically, we organize the massive images set into a large scale graph using graph database, according to the similarity between images. A heuristic method is utilized to map each query image to some vertex in the graph, followed by a local search to refine the query results using an alternative of local random walk on graph. The local random walk process is essentially a weighted partial traversal in the local subgraphs for finding a better match of the query images. We organize the graph of the image set in a parallelization amenable approach, so that a set of partial graph traversal for local random walk can be performed concurrently, taking the advantage of the multithreading capability of processors. We implemented the proposed method in state-of-the-art multicore platforms. The experimental result shows that the graph local random walk based approach outperforms baseline methods in terms of both throughput and scalability.

Date

03 Sep 2014

Publication

ICMEW 2014

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