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arXiv 2022年

作者： Qiu, Dafei Yi, Jiajin Peng, Jialin Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University China Meitu Inc. Xiamen China College of Computer Science and Technology Huaqiao University China

Accurate segmentation of organelle instances is essential for electron microscopy analysis. Despite the outstanding performance of fully supervised methods, they highly rely on sufficient per-pixel annotated data and are sensitive to domain shift. Aiming to develop a highly annotation-efficient approach with competitive performance, we focus on weakly-supervised domain adaptation (WDA) with a type of extremely sparse and weak annotation demanding minimal annotation efforts, i.e., sparse point annotations on only a small subset of object instances. To reduce performance degradation arising from domain shift, we explore multi-level transferable knowledge through conducting three complementary tasks, i.e., counting, detection, and segmentation, constituting a task pyramid with different levels of domain invariance. The intuition behind this is that after investigating a related source domain, it is much easier to spot similar objects in the target domain than to delineate their fine boundaries. Specifically, we enforce counting estimation as a global constraint to the detection with sparse supervision, which further guides the segmentation. A cross-position cut-and-paste augmentation is introduced to further compensate for the annotation sparsity. Extensive validations show that our model with only 15% point annotations can achieve comparable performance as supervised models and shows robustness to annotation selection. © 2022, CC BY-NC-ND.

关键词： Electron microscopy

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MRI denoising using randomized version of nonlocal means method

MRI denoising using randomized version of nonlocal means met...

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2016 International Conference on Image Processing, computer vision, and pattern recognition, IPCV 2016

作者： Hu, Jinrong He, Jia Fu, Ying Wu, Xi Zhou, Jiliu School of Computer and Soft Engineering Xihua University Chengdu610039 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Chengdu University Chengdu610106 China School of Computer Science Chengdu University of Information Technology Chengdu610225 China

ISBN: (纸本)1601324421

- Non-local mean (NLM) algorithm has been implemented effectively in MRI denoising and is always limited by its computational complexity. To reduce the computational burden of NLM in 3D MRI dataset, in this paper, we used a randomized version of NLM algorithm to remove the noise in MRI data. The random NLM algorithm seeds up the classical NLM by computing a small subset of image patch distances, which are randomly select according to the uniform sampling pattern. Numerical experiments demonstrate that the random NLM can achieve a competitive denoising result at a low sampling rate (0.05) in 3D MRI dataset while reducing the runtime dramatically. © CSREA Press.

关键词： Magnetic resonance imaging

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WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy

WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of E...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Dafei Qiu Jiajin Yi Jialin Peng Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University China Meitu Inc. Xiamen China College of Computer Science and Technology Huaqiao University China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

关键词： Degradation Adaptation models Annotations Biological system modeling Estimation Robustness Data models

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Facial landmark localization based on hierarchical pose regression with cascaded random ferns 13

Facial landmark localization based on hierarchical pose regr...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Zhanpeng Zhang, Wei Liu, Jianzhuang Tang, Xiaoou Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Department of Information Engineering Chinese University of Hong Kong Hong Kong Media Lab Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781450324045

The main challenge of facial landmark localization in realworld application is that the large changes of head pose and facial expressions cause substantial image appearance variations. To avoid high dimensional regression in the 3D and 2D facial pose spaces simultaneously, we propose a hierarchical pose regression approach, estimating the head rotation, facial components and landmarks hierarchically. The regression process works in a unified cascaded fern framework. We present generalized gradient boosted ferns (GBFs) for the regression framework, which give better performance than traditional ferns. The framework also achieves real time performance. We verify our method on the latest benchmark datasets. The results show that it outperforms state-of-theart methods in both accuracy and speed. Copyright © 2013 ACM.

关键词： Regression analysis

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Learning Discriminative Representation For Facial Expression recognition From Uncertainties

Learning Discriminative Representation For Facial Expression...

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IEEE International Conference on Image Processing

作者： Xingyu Fan Zhongying Deng Kai Wang Xiaojiang Peng Yu Qiao Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Recent progresses on Facial Expression recognition (FER) heavily rely on deep learning models trained with large scale datasets. However, large-scale facial expression datasets always suffer from annotation uncertainties caused by ambiguous expressions, low-quality facial images, and the subjectiveness of annotators, which limits FER performance. To address this challenge, this paper introduces novel Rayleigh and weighted-softmax loss from two aspects. First, we propose Rayleigh loss to extract discriminative representation, which aims at minimizing within-class distances and maximizing inter-class distances simultaneously. Moreover, Rayleigh loss has a Euclidean form which make it easily be optimized with SGD and be combined with other forms. Second, we introduce a weight to measure the uncertainty of a given sample, by considering its distance to class center. Extensive experiments on RAF-DB, FERPlus and AffectNet show the effectiveness of our method with SOTA performance.

关键词： Weight measurement Emotion recognition Uncertainty Face recognition Image processing Measurement uncertainty Task analysis

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Multi-feature canonical correlation analysis for face photo-sketch image retrieval 13

Multi-feature canonical correlation analysis for face photo-...

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21st ACM International Conference on Multimedia, MM 2013

作者： Gong, Dihong Li, Zhifeng Liu, Jianzhuang Qiao, Yu Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Media Lab Huawei Technologies Co. Ltd. China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450324045

Automatic face photo-sketch image retrieval has attracted great attention in recent years due to its important applications in real life. The major difficulty in automatic face photo-sketch image retrieval lies in the fact that there exists great discrepancy between the different image modalities (photo and sketch). In order to reduce such discrepancy and improve the performance of automatic face photo-sketch image retrieval, we propose a new framework called multi-feature canonical correlation analysis (MCCA) to effectively address this problem. The MCCA is an extension and improvement of the canonical correlation analysis (CCA) algorithm using multiple features combined with two different random sampling methods in feature space and sample space. In this framework, we first represent each photo or sketch using a patch-based local feature representation scheme, in which histograms of oriented gradients (HOG) and multi-scale local binary pattern (MLBP) serve as the local descriptors. Canonical correlation analysis (CCA) is then performed on a collection of random subspaces to construct an ensemble of classifiers for photo-sketch image retrieval. Extensive experiments on two public-domain face photo-sketch datasets (CUFS and CUFSF) clearly show that the proposed approach obtains a substantial improvement over the state-of-the-art. Copyright © 2013 ACM.

关键词： Image retrieval

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Incorporating User Provided Constraints into Document Clustering

Incorporating User Provided Constraints into Document Cluste...

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IEEE International Conference on Data Mining (ICDM)

作者： Yanhua Chen Manjeet Rege Ming Dong Jing Hua Department of Computer Science Machine Vision and Pattern Recognition Laboratory Detroit MI USA Department of Computer Science Graphics and Imaging Laboratory Detroit MI USA

Document clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose SS-NMF: a semi-supervised non- negative matrix factorization framework for document clustering. In SS-NMF, users are able to provide supervision for document clustering in terms of pairwise constraints on a few documents specifying whether they "must" or "cannot" be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the document- document similarity matrix to infer the document clusters. Theoretically, we show that SS-NMF provides a general framework for semi-supervised clustering and that existing approaches can be considered as special cases of SS-NMF. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for clustering documents.

关键词： Iterative algorithms Clustering algorithms Symmetric matrices Data mining Databases Clustering methods Couplings Machine vision pattern recognition computer graphics

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FunnyNet-W: Multimodal Learning of Funny Moments in Videos in the Wild

arXiv

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arXiv 2024年

作者： Liu, Zhi-Song Courant, Robin Kalogeiton, Vicky Computer Vision and Pattern Recognition Laboratory Lappeenranta-Lahti University of Technology Finland LIX Ecole Polytechnique IP Paris France

Automatically understanding funny moments (i.e., the moments that make people laugh) when watching comedy is challenging, as they relate to various features, such as body language, dialogues and culture. In this paper, we propose FunnyNet-W, a model that relies on cross- and self-attention for visual, audio and text data to predict funny moments in videos. Unlike most methods that rely on ground truth data in the form of subtitles, in this work we exploit modalities that come naturally with videos: (a) video frames as they contain visual information indispensable for scene understanding, (b) audio as it contains higher-level cues associated with funny moments, such as intonation, pitch and pauses and (c) text automatically extracted with a speech-to-text model as it can provide rich information when processed by a Large Language Model. To acquire labels for training, we propose an unsupervised approach that spots and labels funny audio moments. We provide experiments on five datasets: the sitcoms TBBT, MHD, MUStARD, Friends, and the TED talk UR-Funny. Extensive experiments and analysis show that FunnyNet-W successfully exploits visual, auditory and textual cues to identify funny moments, while our findings reveal FunnyNet-W’s ability to predict funny moments in the wild. FunnyNet-W sets the new state of the art for funny moment detection with multimodal cues on all datasets with and without using ground truth information. © 2024, CC BY.

关键词： C (programming language)

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Gender Classification from Offline Handwriting Images Using Textural Features

Gender Classification from Offline Handwriting Images Using ...

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International Workshop on Frontiers in Handwriting recognition

作者： Ali Mirza Momina Moetesum Imran Siddiqi Chawki Djeddi Center of Computer Vision and Pattern Recognition Bahria University Islamabad Pakistan LAMIS Laboratory Larbi Tebessi University Tebessa Algeria

ISBN: (纸本)9781509009824

Prediction of gender and other demographic attributes of individuals from handwriting samples offers an interesting basic, as well as applied research problem. The correlation between gender and the visual appearance of handwriting has been validated by a number of studies and the present study is based on the same idea. We exploit the textural measurements as the discriminating attribute between male and female writings. The textural information in a writing is captured by applying a bank of Gabor filters to the image of handwriting. The mean and standard deviation values of the filter responses are collected in matrix and the Fourier transform of the matrix is used as a feature. Classification is carried out using a feed forward neural network. The proposed technique evaluated on a subset of the QUWI database realized promising results under different experimental settings.

关键词： Writing Databases Feature extraction Visualization Training Correlation Standards

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New Moments Based Fuzzy Similarity Measure for Text Detection in Distorted Social Media Images 5th

New Moments Based Fuzzy Similarity Measure for Text Detectio...

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5th Asian Conference on pattern recognition, ACPR 2019

作者： Roy, Soumyadip Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Blumenstein, Michael Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Engineering and Information Technology University of Technology Sydney Australia

ISBN: (纸本)9783030414030

A trend towards capturing or filming images using cellphone and sharing images on social media is a part and parcel of day to day activities of humans. When an image is forwarded several times in social media it may be distorted a lot due to several different devices. This work deals with text detection from such distorted images. In this work, we consider images pass through three mobile devices on WhatsApp social media, which results in four images (including the original image) Unlike the existing methods that aim at developing new ways, we utilize the results detected by the existing ones to improve performances. The proposed method extracts Hu moments and fuzzy logic from detected texts of images. The similarity between text detection results given by three existing text detection methods is studied for determining the best pair of texts. The same similarity estimation is then used in a novel way to remove extra background or non-texts and restoring missing text information. Experimental results on own dataset and benchmark datasets of natural scene images, namely, MSRA-TD500, ICDAR2017-MLT, Total-Text, CTW1500 dataset and COCO datasets, show that the proposed method outperforms the existing methods. © Springer Nature Switzerland AG 2020.

关键词： Fuzzy logic

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