Color and Edge Directivity Descriptor on GPGPU

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing Pub Date : 2015-03-04 DOI:10.1109/PDP.2015.105

C. Iakovidou, Loukas Bampis, S. Chatzichristofis, Y. Boutalis, A. Amanatiadis

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引用次数: 3

Abstract

Image indexing refers to describing the visual multimedia content of a medium, using high level textual information or/and low level descriptors. In most cases, images and videos are associated with noisy and incomplete user-supplied textual annotations, possibly due to omission or the excessive cost associated with the metadata creation. In such cases, Content Based Image Retrieval (CBIR) approaches are adopted and low level image features are employed for indexing and retrieval. We employ the Colour and Edge Directivity Descriptor (CEDD), which incorporates both colour and texture information in a compact representation and reassess it for parallel execution, utilizing the multicore power provided by General Purpose Graphic Processing Units (GPGPUs). Experiments conducted on four different combinations of GPU-CPU technologies revealed an impressive gained acceleration when using a GPU, which was up to 22 times faster compared to the respective CPU implementation, while real-time indexing was achieved for all tested GPU models.

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GPGPU上的颜色和边缘方向性描述符

图像索引是指使用高级文本信息或/和低级描述符来描述媒体的视觉多媒体内容。在大多数情况下，图像和视频与嘈杂和不完整的用户提供的文本注释相关联，这可能是由于遗漏或与元数据创建相关的过高成本。在这种情况下，采用基于内容的图像检索(CBIR)方法，利用低级图像特征进行索引和检索。我们采用颜色和边缘方向性描述符(CEDD)，它将颜色和纹理信息合并在一个紧凑的表示中，并利用通用图形处理单元(gpgpu)提供的多核功能对其进行重新评估以进行并行执行。在四种不同的GPU-CPU技术组合上进行的实验显示，使用GPU时获得了令人印象深刻的加速，与各自的CPU实现相比，速度快了22倍，同时所有测试的GPU模型都实现了实时索引。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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