GPU Acceleration of Content-Based Image Retrieval Based on SIFT Descriptors

Kusamura, Yuta; Kozawa, Yusuke; Amagasa, Toshiyuki; Kitagawa, Hiroyuki

doi:10.1109/nbis.2016.55

Cited by 9 publications

(6 citation statements)

References 14 publications

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“…Additionally, systems can contain multiple processing units, such as multi GPUs. State-ofthe-art approaches [40][41][42] mainly apply GPUs for performance improvements.…”

Section: Parallel Computingmentioning

confidence: 99%

Parallelization Strategies for Graph-Code-Based Similarity Search

Steinert

Wagenpfeil

Kevitt³

et al. 2023

BDCC

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The volume of multimedia assets in collections is growing exponentially, and the retrieval of information is becoming more complex. The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. Feature graphs contain semantic information on multimedia assets. Machine learning can produce detailed semantic information on multimedia assets, reflected in a high volume of nodes and edges in the feature graphs. While increasing the effectiveness of the information retrieval results, the high level of detail and also the growing collections increase the processing time. Addressing this problem, Multimedia Feature Graphs (MMFGs) and Graph Codes (GCs) have been proven to be fast and effective structures for information retrieval. However, the huge volume of data requires more processing time. As Graph Code algorithms were designed to be parallelizable, different paths of parallelization can be employed to prove or evaluate the scalability options of Graph Code processing. These include horizontal and vertical scaling with the use of Graphic Processing Units (GPUs), Multicore Central Processing Units (CPUs), and distributed computing. In this paper, we show how different parallelization strategies based on Graph Codes can be combined to provide a significant improvement in efficiency. Our modeling work shows excellent scalability with a theoretical speedup of 16,711 on a top-of-the-line Nvidia H100 GPU with 16,896 cores. Our experiments with a mediocre GPU show that a speedup of 225 can be achieved and give credence to the theoretical speedup. Thus, Graph Codes provide fast and effective multimedia indexing and retrieval, even in billion-scale use cases.

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“…Additionally, systems can contain multiple processing units, such as multi GPUs. State-ofthe-art approaches [40][41][42] mainly apply GPUs for performance improvements.…”

Section: Parallel Computingmentioning

confidence: 99%

Parallelization Strategies for Graph-Code-Based Similarity Search

Steinert

Wagenpfeil

Kevitt³

et al. 2023

BDCC

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“…The directional differential channels for the four directions are as shown in Figure 1b. The second order directional [2] LDP, for the pixel 0 ∝ 2 ( 0 ), in α direction is given as in (5). where (.…”

Section: Ldp Descriptor and Usage For Image Retrievalmentioning

confidence: 99%

“…GPUs are inexpensive, scalable and have very high computation power compared to single processor CPUs. GPUs have been widely used now for accelerating many efficient CBIR algorithms [4], [5] in the field of image vision, medical imaging [6], remote sensing [7] etc. This paper proposes a parallel implementation of the LDP algorithm on NVIDIA GPUs.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs

Chandran

Gangodkar

Mittal

2018

IJECE

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<p><span>Pattern based texture descriptors are widely used in Content Based Image Retrieval (CBIR) for efficient retrieval of matching images. Local Derivative Pattern (LDP), a higher order local pattern operator, originally proposed for face recognition, encodes the distinctive spatial relationships contained in a local region of an image as the feature vector. LDP efficiently extracts finer details and provides efficient retrieval however, it was proposed for images of limited resolution. Over the period of time the development in the digital image sensors had paid way for capturing images at a very high resolution. LDP algorithm though very efficient in content-based image retrieval did not scale well when capturing features from such high-resolution images as it becomes computationally very expensive. This paper proposes how to efficiently extract parallelism from the LDP algorithm and strategies for optimally implementing it by exploiting some inherent General-Purpose Graphics Processing Unit (GPGPU) characteristics. By optimally configuring the GPGPU kernels, image retrieval was performed at a much faster rate. The LDP algorithm was ported on to Compute Unified Device Architecture (CUDA) supported GPGPU and a maximum speed up of around 240x was achieved as compared to its sequential counterpart.</span></p>

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“…In pursuing real-time applications, some GPU-accelerated algorithms were proposed to reduce the time consumption. Reference [33] proposed GPU-accelerated KAZE, which is about 10× faster than the CPU-version of KAZE; reference [34] implemented a GPU version of SIFT, with an accelerated factor of 2.5×. However, GPU is merely a tool for acceleration; it does not reduce the complexity of these algorithms.…”

Section: Introductionmentioning

confidence: 99%

IFRAD: A Fast Feature Descriptor for Remote Sensing Images

et al. 2021

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Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance.

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GPU Acceleration of Content-Based Image Retrieval Based on SIFT Descriptors

Cited by 9 publications

References 14 publications

Parallelization Strategies for Graph-Code-Based Similarity Search

Parallelization Strategies for Graph-Code-Based Similarity Search

Real-Time Implementation and Performance Optimization of Local Derivative Pattern Algorithm on GPUs

IFRAD: A Fast Feature Descriptor for Remote Sensing Images

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