BRIEF: Binary Robust Independent Elementary Features

Calonder, Michael; Lepetit, Vincent; Strecha, Christoph; Fua, Pascal

doi:10.1007/978-3-642-15561-1_56

Cited by 2,699 publications

(1,860 citation statements)

References 15 publications

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“…SURF descriptors can be extracted much faster by making an approximation of SIFT descriptors [10], but even these cannot be extracted as fast as binary descriptors. The binary descriptors such as CARD, ORB, and BRISK obtain binary values by using simple binary tests between pixels in a smoothed image patch instead of computing gradients from the patch [3]- [5], [9]. [6].…”

Section: Local Descriptor Extraction and Descriptor Aggregationmentioning

confidence: 99%

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Image Retrieval Framework Based on Dual Representation Descriptor

Yoshida

Toyofuku

2017

IEICE Trans. Inf. & Syst.

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SUMMARYDescriptor aggregation techniques such as the Fisher vector and vector of locally aggregated descriptors (VLAD) are used in most image retrieval frameworks. It takes some time to extract local descriptors, and the geometric verification requires storage if a real-valued descriptor such as SIFT is used. Moreover, if we apply binary descriptors to such a framework, the performance of image retrieval is not better than if we use a real-valued descriptor. Our approach tackles these issues by using a dual representation descriptor that has advantages of being both a real-valued and a binary descriptor. The real value of the dual representation descriptor is aggregated into a VLAD in order to achieve high accuracy in the image retrieval, and the binary one is used to find correspondences in the geometric verification stage in order to reduce the amount of storage needed. We implemented a dual representation descriptor extracted in semi-real time by using the CARD descriptor. We evaluated the accuracy of our image retrieval framework including the geometric verification on three datasets (holidays, ukbench and Stanford mobile visual search). The results indicate that our framework is as accurate as the framework that uses SIFT. In addition, the experiments show that the image retrieval speed and storage requirements of our framework are as efficient as those of a framework that uses ORB. key words: image retrieval, local descriptor

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Section: Local Descriptor Extraction and Descriptor Aggregationmentioning

confidence: 99%

“…The binary descriptors such as ORB and BRISK that have bits at each keypoint [3]- [5]. Aggregation vectors with binary descriptors are not as robust as aggregation vectors with real-valued descriptors such as SIFT [6].…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval Framework Based on Dual Representation Descriptor

Yoshida

Toyofuku

2017

IEICE Trans. Inf. & Syst.

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“…Therefore, binary descriptors have become more attractive in recent years, since they are compact and faster to compare using Hamming metric. In most cases, handcrafted binary descriptors are obtained using pairwise tests between intensities of predefined parts of described image patch, i.e., pixels or regions according to a sampling pattern [4][5][6][7][8]. However, binary descriptors can be long, what requires an additional procedure for their reduction.…”

Section: Introductionmentioning

confidence: 99%

An optimisation approach to the design of a fast, compact and distinctive binary descriptor

Oszust

2016

SIViP

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The desired local feature descriptor should be distinctive, compact and fast to compute and match. Therefore, many computer vision applications use binary keypoint descriptors instead of floating-point, rich techniques. In this paper, an optimisation approach to the design of a binary descriptor is proposed, in which the detected keypoint is described using several, scale-dependent patches. Each such patch is divided into disjoint blocks of pixels, and then, binary tests between blocks' intensities, as well as their gradients, are used to obtain the binary string. Since the number of image patches and their relative sizes influence the descriptor creation pipeline, a simulated annealing algorithm is used to determine them, optimising recall and precision of keypoint matching. The simulated annealing is also used for dimensionality reduction in long binary strings. The proposed approach is extensively evaluated and compared with SIFT, SURF and BRIEF on public benchmarks. Obtained results show that the binary descriptor created using the resulted pipeline is faster to compute and yields comparable or better performance than the state-of-the-art descriptors under different image transformations.

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“…Calonder et al [3] proposed efficient BRIEF (Binary Robust Independent Elementary Features) descriptors that are computed using simple intensity comparisons at random pre-determined pixel locations. The performance of BRIEF is similar to SIFT in many respects, including robustness to lighting, blur, and perspective distortion.…”

Section: Introductionmentioning

confidence: 99%

CBRISK: Colored Binary Robust Invariant Scalable Keypoints

Jing

Han

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYBRISK (Binary Robust Invariant Scalable Keypoints) works dramatically faster than well-established algorithms (SIFT and SURF) while maintaining matching performance. However BRISK relies on intensity, color information in the image is ignored. In view of the importance of color information in vision applications, we propose CBRISK, a novel method for taking into account color information during keypoint detection and description. Instead of grayscale intensity image, the proposed approach detects keypoints in the photometric invariant color space. On the basis of binary intensity BRISK (original BRISK) descriptor, the proposed approach embeds binary invariant color presentation in the CBRISK descriptors. Experimental results show that CBRISK is more discriminative and robust than BRISK with respect to photometric variation. key words: color space, photometric invariant, binary descriptor

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BRIEF: Binary Robust Independent Elementary Features

Cited by 2,699 publications

References 15 publications

Image Retrieval Framework Based on Dual Representation Descriptor

Image Retrieval Framework Based on Dual Representation Descriptor

An optimisation approach to the design of a fast, compact and distinctive binary descriptor

CBRISK: Colored Binary Robust Invariant Scalable Keypoints

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