A Convolutional Treelets Binary Feature Approach to Fast Keypoint Recognition

Wu, Chenxia; Zhu, Jianke; Zhang, Jiemi; Chen, Chun

doi:10.1007/978-3-642-33715-4_27

Cited by 7 publications

(7 citation statements)

References 20 publications

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“…All recent performance evaluation protocols measure tracking accuracy primarily by intersection over union (IoU) between the ground truth and tracker prediction bounding boxes. A legacy center-based measure initially promoted by Babenko et al [3] and later adopted by [90] is still often used, but is theoretically brittle and inferior to the overlap-based measure [83]. In the no-reset-based protocols the overall performance is summarized by the average IoU over the dataset (i.e., average overlap) [90,83].…”

Section: Related Work In Short-term Trackingmentioning

confidence: 99%

“…A legacy center-based measure initially promoted by Babenko et al [3] and later adopted by [90] is still often used, but is theoretically brittle and inferior to the overlap-based measure [83]. In the no-reset-based protocols the overall performance is summarized by the average IoU over the dataset (i.e., average overlap) [90,83]. In the VOT reset-based protocols, two measures are used to probe the performance: (i) accuracy and (ii) robustness.…”

Section: Related Work In Short-term Trackingmentioning

confidence: 99%

“…But center-error-based measures are even more brittle than IoU-based measures, are resolution-dependent and are computed only in frames where the target is present and the tracker reports its position. Thus most papers published in the last few years (e.g, [34,57,20]) have simply used the short-term average overlap performance measure from [90,61]. But this measure does not account for the tracker's ability to correctly report target absence and favors reporting target positions at every frame.…”

Section: Related Work In Long-term Trackingmentioning

confidence: 99%

See 2 more Smart Citations

The Sixth Visual Object Tracking VOT2018 Challenge Results

Kristan¹,

Leonardis²,

Matas³

et al. 2019

Lecture Notes in Computer Science

456

674

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The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new longterm tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 60 .

show abstract

Section: Related Work In Short-term Trackingmentioning

confidence: 99%

Section: Related Work In Short-term Trackingmentioning

confidence: 99%

Section: Related Work In Long-term Trackingmentioning

confidence: 99%

See 1 more Smart Citation

The Sixth Visual Object Tracking VOT2018 Challenge Results

Kristan¹,

Leonardis²,

Matas³

et al. 2019

Lecture Notes in Computer Science

456

674

View full text Add to dashboard Cite

show abstract

“…The second group extracts the binary strings from image patches [9]- [12], [26], [27] in order to further speed up the process of feature extraction and matching. A recent evaluation on the binary feature descriptors can be found in [28].…”

Section: Related Workmentioning

confidence: 99%

Treelets Binary Feature Retrieval for Fast Keypoint Recognition

Zhu

Chen

2015

IEEE Trans. Cybern.

Self Cite

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Fast keypoint recognition is essential to many vision tasks. In contrast to the classification-based approaches, we directly formulate the keypoint recognition as an image patch retrieval problem, which enjoys the merit of finding the matched keypoint and its pose simultaneously. To effectively extract the binary features from each patch surrounding the keypoint, we make use of treelets transform that can group the highly correlated data together and reduce the noise through the local analysis. Treelets is a multiresolution analysis tool, which provides an orthogonal basis to reflect the geometry of the noise-free data. To facilitate the real-world applications, we have proposed two novel approaches. One is the convolutional treelets that capture the image patch information locally and globally while reducing the computational cost. The other is the higher-order treelets that reflect the relationship between the rows and columns within image patch. An efficient sub-signature-based locality sensitive hashing scheme is employed for fast approximate nearest neighbor search in patch retrieval. Experimental evaluations on both synthetic data and the real-world Oxford dataset have shown that our proposed treelets binary feature retrieval methods outperform the state-of-the-art feature descriptors and classification-based approaches.

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“…A lot of hashing algorithms have been proposed for various applications, such as image and video retrieval [28,27,31,12], duplicate image detection [3], key point detection [29] and so on.…”

Section: Introductionmentioning

confidence: 99%

Iterative Multi-View Hashing for Cross Media Indexing

Jin

Ren

et al. 2014

Proceedings of the 22nd ACM International Conference on Multimedia

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Cross media retrieval engines have gained massive popularity with rapid development of the Internet. Users may perform queries in a corpus consisting of audio, video, and textual information. To make such systems practically possible for large mount of multimedia data, two critical issues must be carefully considered: (a) reduce the storage as much as possible; (b) model the relationship of the heterogeneous media data. Recently academic community have proved that encoding the data into compact binary codes can drastically reduce the storage and computational cost. However, it is still unclear how to integrate multiple information sources properly into the binary code encoding scheme.In this paper, we study the cross media indexing problem by learning the discriminative hashing functions to map the multi-view datum into a shared hamming space. Not only meaningful withinview similarity is required to be preserved, we also incorporate the between-view correlations into the encoding scheme, where we map the similar points close together and push apart the dissimilar ones. To this end, we propose a novel hashing algorithm called Iterative Multi-View Hashing (IMVH) by taking these information into account simultaneously. To solve this joint optimization problem efficiently, we further develop an iterative scheme to deal with it by using a more flexible quantization model. In particular, an optimal alignment is learned to maintain the between-view similarity in the encoding scheme. And the binary codes are obtained by directly solving a series of binary label assignment problems without continuous relaxation to avoid the unnecessary quantization loss. In this way, the proposed algorithm not only greatly improves the retrieval accuracy but also performs strong robustness. An extensive set of experiments clearly demonstrates the superior performance of the proposed method against the state-of-the-art techniques on both multimodal and unimodal retrieval tasks.

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A Convolutional Treelets Binary Feature Approach to Fast Keypoint Recognition

Cited by 7 publications

References 20 publications

The Sixth Visual Object Tracking VOT2018 Challenge Results

The Sixth Visual Object Tracking VOT2018 Challenge Results

Treelets Binary Feature Retrieval for Fast Keypoint Recognition

Iterative Multi-View Hashing for Cross Media Indexing

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