Efficient extraction of clustering-based feature signatures using GPU architectures

Abstract:

Similarity search and content-based retrieval have become widely used in multimedia database systems that often manage huge data collections. Unfortunately, many effective content-based similarity models cannot be fully utilized for larger datasets, as they are computationally demanding and require massive parallel processing for both feature extraction and query evaluation tasks. In this work, we address the performance issues of effective similarity models based on feature signatures, where we focus on fast feature extraction from image thumbnails using affordable hardware. More specifically, we propose a multi-GPU implementation that increases the extraction speed by two orders of magnitude with respect to a single-threaded CPU implementation. Since the extraction algorithm is not directly parallelizable, we propose a modification of the algorithm embracing the SIMT execution model. We have experimentally verified that our GPU extractor can be successfully used to index large image datasets comprising millions of images. In order to obtain optimal extraction parameters, we employed the GPU extractor in an extensive empirical investigation of the parameter space. The experimental results are discussed from the perspectives of both performance and similarity precision.

BibTeX:

@article{krulis_efficient_2016,
    title = {{Efficient extraction of clustering-based feature signatures using GPU architectures}},
    author = {Kruliš, Martin and Lokoč, Jakub and Skopal, Tomáš},
    year = {2016},
    journal = {{Multimedia Tools and Applications}},
    number = {13},
    doi = {10.1007/s11042-015-2726-y},
    issn = {1573-7721},
    pages = {8071--8103},
    url = {https://doi.org/10.1007/s11042-015-2726-y},
    volume = {75},
}

Journal article