Cross-Modality Bridging and Knowledge Transferring for Image Understanding

Yan, Chenggang; Li, Liang; Zhang, Chunjie; Liu, Bingtao; Zhang, Yongdong; Dai, Qionghai

doi:10.1109/tmm.2019.2903448

Cited by 163 publications

(46 citation statements)

References 58 publications

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“…Our future work will be focused on utilizing these unique characteristics of HEVC to detect double compression. Meanwhile, the application of emerging new techniques in related area may boost the development of HEVC double compression detection [22,23].…”

Section: Discussionmentioning

confidence: 99%

HEVC double compression detection under different bitrates based on TU partition type

Yang

et al. 2019

J Image Video Proc.

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During the process of authenticating the integrity of digital videos, double compression is an important evidence. High-efficiency video coding (HEVC) is the latest coding standard proposed in 2013 and has shown superior performance over its predecessors. It brings in several novel syntactic units, such as coding tree unit (CTU), prediction unit (PU), and transform unit (TU). Few methods have been reported to detect HEVC double compression by utilizing such new characteristics. In this paper, a novel scheme based on TU is proposed to detect double compression of HEVC videos. The histogram of each TU partition type in the first I/P frame of all GOPs is calculated. The feature set is fed into SVM to classify single and double compressed videos. Experimental results have demonstrated the effectiveness of TU-based feature (accuracy in [0.84, 0.97]) that bears very low dimension (10-D). And it is also shown that TU-based feature set can be combined with other feature sets to boost their performance.

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Section: Discussionmentioning

confidence: 99%

HEVC double compression detection under different bitrates based on TU partition type

Yang

et al. 2019

J Image Video Proc.

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“…The image retrieval mentioned in this paper refers to content-based visual information retrieval [26], [27], [28]. This process can be simply expressed as: feeding the unlabeled original images to a deep net architecture or other retrieval methods to get images which are similar or belong to the same category of the inputs.…”

Section: Approximate Nearest Searching With Hashingmentioning

confidence: 99%

Deep Multi-View Enhancement Hashing for Image Retrieval

Yan

Gong

Wei

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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332

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Hashing is an efficient method for nearest neighbor search in large-scale data space by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. However, large-scale high-speed retrieval through binary code has a certain degree of reduction in retrieval accuracy compared to traditional retrieval methods. We have noticed that multi-view methods can well preserve the diverse characteristics of data. Therefore, we try to introduce the multi-view deep neural network into the hash learning field, and design an efficient and innovative retrieval model, which has achieved a significant improvement in retrieval performance. In this paper, we propose a supervised multi-view hash model which can enhance the multi-view information through neural networks. This is a completely new hash learning method that combines multi-view and deep learning methods. The proposed method utilizes an effective view stability evaluation method to actively explore the relationship among views, which will affect the optimization direction of the entire network. We have also designed a variety of multi-data fusion methods in the Hamming space to preserve the advantages of both convolution and multi-view. In order to avoid excessive computing resources on the enhancement procedure during retrieval, we set up a separate structure called memory network which participates in training together. The proposed method is systematically evaluated on the CIFAR-10, NUS-WIDE and MS-COCO datasets, and the results show that our method significantly outperforms the state-of-the-art single-view and multi-view hashing methods.

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“…Descriptors encode important information about the visual characteristics of the objects present in images [34], such as appearance [35,36], motion [37], or geometry [38]. Therefore, they have been used in multiple contexts.…”

Section: Descriptorsmentioning

confidence: 99%

Robust head pose estimation based on key frames for human-machine interaction

Madrigal

Lerasle

2020

J Image Video Proc.

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Humans can interact with several kinds of machine (motor vehicle, robots, among others) in different ways. One way is through his/her head pose. In this work, we propose a head pose estimation framework that combines 2D and 3D cues using the concept of key frames (KFs). KFs are a set of frames learned automatically offline that consist the following: 2D features, encoded through Speeded Up Robust Feature (SURF) descriptors; 3D information, captured by Fast Point Feature Histogram (FPFH) descriptors; and target's head orientation (pose) in real-world coordinates, which is represented through a 3D facial model. Then, the KF information is re-enforced through a global optimization process that minimizes error in a way similar to bundle adjustment. The KF allows to formulate, in an online process, a hypothesis of the head pose in new images that is then refined through an optimization process, performed by the iterative closest point (ICP) algorithm. This KF-based framework can handle partial occlusions and extreme rotations even with noisy depth data, improving the accuracy of pose estimation and detection rate. We evaluate the proposal using two public benchmarks in the state of the art: (1) BIWI Kinect Head Pose Database and (2) ICT 3D HeadPose Database. In addition, we evaluate this framework with a small but challenging dataset of our own authorship where the targets perform more complex behaviors than those in the aforementioned public datasets. We show how our approach outperforms relevant state-of-the-art proposals on all these datasets.

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Cross-Modality Bridging and Knowledge Transferring for Image Understanding

Cited by 163 publications

References 58 publications

HEVC double compression detection under different bitrates based on TU partition type

HEVC double compression detection under different bitrates based on TU partition type

Deep Multi-View Enhancement Hashing for Image Retrieval

Robust head pose estimation based on key frames for human-machine interaction

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