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Learning a Probabilistic Similarity Function for Segmentation

Learning a Probabilistic Similarity Function for Segmentatio...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： C. Stauffer Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

There are many methods for measuring "similarity" of or "distance" between two image pixels or image patches. These methods generally involve computing some informative features of the image patches and then describing distances between patches based on simple functions of those feature values, e.g., Euclidean distance. Unfortunately, these measures are often frail and difficult to interpret. The goal of this paper is to learn the "similarity" of two patches as an approximation to the likelihood that two patches were drawn from the same surface in the world. This measure is well-defined and allows for a maximization of meaningful values when it is combined with common segmentation algorithms. We introduce a general approximation technique that involves learning a codebook, learning the likelihood function for pairs of codebook entries, and applying the resulting likelihood function in segmentation tasks. These steps can be performed independently, even on different data sets drawn from the same domain. The likelihood can be learned from pre-segmented image set or heuristically approximated from an unsegmented image set. We show examples of probabilistic segmentation of the codebooks themselves based on our similarity measure for multiple types of codebooks including color codebooks and Epitome codebooks. These segmentations illustrate the usefulness of our technique as an image patch similarity measure.

关键词： Image segmentation Pixel Euclidean distance Focusing Clustering algorithms Application software Learning computer science Artificial intelligence Laboratories

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Graph Embedding Based Semi-supervised Discriminative Tracker

Graph Embedding Based Semi-supervised Discriminative Tracker

引用

International conference on computer vision workshops (ICCV workshops)

作者： Jin Gao Junliang Xing Weiming Hu Xiaoqin Zhang National Laboratory of Pattern Recognition Institute of Automation China Institute of Intelligent System and Decision Wenzhou University China

Recently, constructing a good graph to represent data structures is widely used in machine learning based applications. Some existing trackers have adopted graph construction based classifiers for tracking. However, their graph structures are not effective to characterize the inter-class separability and multi-model sample distribution, both of which are very important to successful tracking. In this paper, we propose to use a new graph structure to improve tracking performance without the assistance of learning object subspace generatively as previous work did. Meanwhile, considering the test samples deviate from the distribution of the training samples in tracking applications, we formulate the discriminative learning process, to avoid over fitting, in a semi-supervised fashion as L1-graph based regularizer. In addition, a non-linear variant is extended to adapt to multi-modal sample distribution. Experimental results demonstrate the superior properties of the proposed tracker.

关键词： Vectors Training Feature extraction Hilbert space Robustness Noise Covariance matrices

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Background Subtraction Based on a Robust Consensus Method

Background Subtraction Based on a Robust Consensus Method

引用

International conference on pattern recognition

作者： Hanzi Wang D. Suter Institute for Vision Systems Engineering Department of Electrical and Computer Systems Engineering Monash University Clayton VIC Australia

ISBN: (纸本)0769525210

Statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we presents an effective and adaptive background modeling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model per pixel. Numerous experiments on both indoor and outdoor video sequences show that the proposed method, compared with several state-of-the-art methods, can achieve very promising performance

关键词： Robustness computer vision Systems engineering and theory Application software Layout Pixel Machine vision Object detection Video sequences Wiener filter

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Medium Scale Benchmark for Cricket Excited Actions Understanding

Medium Scale Benchmark for Cricket Excited Actions Understan...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Altaf Hussain Noman Khan Muhammad Munsif Min Je Kim Sung Wook Baik Sejong University Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Sports Action recognition (SAR) domain is of significant importance in research, with diverse applications, ranging from aiding coaches in strategic decision-making to empowering athletes and contributing to real-time commercial entertainment. Despite the existence of extensive large-scale and small-scale datasets, the direct application of these datasets to specific sports domains, such as cricket, poses challenges. Existing datasets predominantly center around daily life actions, lacking the necessary granularity for in-depth sports analyses. Current Cricket Action Analysis (CAA) datasets have limitations, including their small scale, modality constraints, and their narrow focus on specific aspects, such as cricket batting. Recognizing the need for a more comprehensive benchmark, this article introduces the Cricket Excited Actions (CEA) dataset. Developed in collaboration with professional cricket players, the CEA dataset encompasses challenging multi-person actions within realistic cricket scenarios. The selected activity classes, such as Clean Bowled, Six, Four, and Catches, adhere to official standards and represent pivotal moments in cricket matches. Through precise annotation and empirical studies, utilizing state-of-the-art action recognition model architectures, this study provides a valuable resource for further research and makes significant contributions by offering support essential to advancing CAA within the cricket sports community. The data and code are available at https://***/Altaf-hucn/Cricket-Excited-Actions-Benchmark.

关键词： Computational modeling Refining Collaboration computer architecture Benchmark testing Real-time systems pattern recognition

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Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble

Divide and Conquer: High-Resolution Industrial Anomaly Detec...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Blaž Rolih Dick Ameln Ashwin Vaidya Samet Akcay Faculty of Computer and Information Science University of Ljubljana Intel

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Industrial anomaly detection is an important task within computer vision with a wide range of practical use cases. The small size of anomalous regions in many real-world datasets necessitates processing the images at a high resolution. This frequently poses significant challenges concerning memory consumption during the model training and inference stages, leaving some existing methods impractical for widespread adoption. To overcome this challenge, we present the tiled ensemble approach, which reduces memory consumption by dividing the input images into a grid of tiles and training a dedicated model for each tile location. The tiled ensemble is compatible with any existing anomaly detection model without the need for any modification of the underlying architecture. By introducing overlapping tiles, we utilize the benefits of traditional stacking ensembles, leading to further improvements in anomaly detection capabilities beyond high resolution alone. We perform a comprehensive analysis using diverse underlying architectures, including Padim, PatchCore, FastFlow, and Reverse Distillation, on two standard anomaly detection datasets: MVTec and VisA. Our method demonstrates a notable improvement across setups while remaining within GPU memory constraints, consuming only as much GPU memory as a single model needs to process a single tile. 1 2

关键词： Training computer vision Image resolution Tiles Memory management Stacking Graphics processing units

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Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

Histopathological Image Classification with Cell Morphology ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Andrey Ignatov Josephine Yates Valentina Boeva Computer Vision Lab ETH Zurich Computational Cancer Genomics Group

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Histopathological images are widely used for the analysis of diseased (tumor) tissues and patient treatment selection. While the majority of microscopy image processing was previously done manually by pathologists, recent advances in computer vision allow for accurate recognition of lesion regions with deep learning-based solutions. Such models, however, usually require extensive annotated datasets for training, which is often not the case in the considered task, where the number of available patient data samples is very limited. To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types. The model consists of two modules: the first one performs cell nuclei segmentation and annotates each cell type, and is trained on a combination of 8 publicly available datasets to ensure its high generalizability and robustness. The second module combines the obtained segmentation map with the original microscopy image and is trained for the downstream task. We pre-trained this module on the Pan-Cancer TCGA dataset consisting of over 270K tissue patches extracted from 8736 diagnostic slides from 7175 patients. The proposed solution achieved a new state-of-the-art performance on the dataset under consideration, detecting 32 cancer types with over 82% accuracy and outperforming all previously proposed solutions by more than 4%. We demonstrate that the resulting pre-trained model can be easily fine-tuned on smaller microscopy datasets, yielding superior results compared to the current top solutions and models initialized with ImageNet weights. The codes and pre-trained models presented in this paper are available at: https://***/aiff22/DeepCMorph

关键词： Image segmentation computer vision Analytical models Accuracy Microscopy Microprocessors Computational modeling

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Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Learning Generative Visual Models from Few Training Examples...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Li Fei-Fei R. Fergus P. Perona California Institute of Technology USA Oxford University UK

Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been tested on more than a handful of object categories. We present an method for learning object categories from just a few training images. It is quick and it uses prior information in a principled way. We test it on a dataset composed of images of objects belonging to 101 widely varied categories. Our proposed method is based on making use of prior information, assembled from (unrelated) object categories which were previously learnt. A generative probabilistic model is used, which represents the shape and appearance of a constellation of features belonging to the object. The parameters of the model are learnt incrementally in a Bayesian manner. Our incremental algorithm is compared experimentally to an earlier batch Bayesian algorithm, as well as to one based on maximum-likelihood. The incremental and batch versions have comparable classification performance on small training sets, but incremental learning is significantly faster, making real-time learning feasible. Both Bayesian methods outperform maximum likelihood on small training sets.

关键词： Bayesian methods Testing Humans Maximum likelihood estimation Assembly Shape Machine vision Image recognition Parameter estimation Image databases

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Enhancing the Transferability of Adversarial Attacks with Stealth Preservation

Enhancing the Transferability of Adversarial Attacks with St...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xinwei Zhang Tianyuan Zhang Yitong Zhang Shuangcheng Liu School of Computer Science and Engineering Beihang University Beijing China State Key Lab of Software Development Environment Beihang University Beijing China Shen Yuan Honors College Beihang University Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks are susceptible to attacks from adversarial examples in recent years. Especially, the black-box attacks cause a more serious threat to practical applications. However, while most existing black-box attacks have achieved a high success rate in deceiving models, they have not focused on the stealthiness of adversarial examples, often exhibiting suspicious visual appearances. To address this issue, this paper proposes the Mask Momentum Iterative Attack (MMIA), which introduces a masking mechanism and adopts an optimal perturbation strategy to identify regions of an image most vulnerable to attacks. This approach effectively ensures the transferability and stealthiness of adversarial examples. Simultaneously, by integrating image enhancement techniques and temporal and spatial momentum terms into the iterative process of the attack, we prevent the attack from getting stuck in local optima, further improving the transferability of adversarial examples. To enhance the success rate of black-box attacks, we apply MMIA to a model ensemble using a joint optimization strategy. We demonstrate that adversarially trained models with a strong defense ability are also susceptible to our black-box attacks. We conduct extensive experiments on classification tasks using common vision models, and our results significantly demonstrate the superiority of our method over state-of-the-art approaches when considering both transferability and stealthiness.

关键词： Visualization Fuses Computational modeling Perturbation methods Closed box pattern recognition Iterative methods

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Learning in Region-Based Image Retrieval with Generalized Support Vector Machines

Learning in Region-Based Image Retrieval with Generalized Su...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： I. Gondra D.R. Heisterkamp Computer Science Department Oklahoma State University Stillwater OK USA

Relevance feedback approaches based on support vector machine (SVM) learning have been applied to significantly improve retrieval performance in content-based image retrieval (CBIR). Those approaches require the use of fixed-length image representations because SVM kernels represent an inner product in a feature space that is a non-linear transformation of the input space. Many region-based CBIR approaches create a variable length image representation and define a similarity measure between two variable length representations. The standard SVM approach cannot be applied to this approach because it violates the requirements that SVM places on the kernel. Fortunately, a generalized SVM (GSVM) has been developed that allows the use of an arbitrary kernel. In this paper, we present an initial investigation into utilizing a GSVM-based relevance feedback learning algorithm. Since GSVM does not place restrictions on the kernel, any image similarity measure can be used. In particular, the proposed approach uses an image similarity measure developed for region-based, variable length representations. Experimental results over real world images demonstrate the efficacy of the proposed method.

关键词： Machine learning Image retrieval Support vector machines Image segmentation Content based retrieval Kernel Feedback computer science Image representation Length measurement

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Recursive Joint Cross-Modal Attention for Multimodal Fusion in Dimensional Emotion recognition

Recursive Joint Cross-Modal Attention for Multimodal Fusion ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： R. Gnana Praveen Jahangir Alam Computer Research Institute of Montreal (CRIM) Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though multimodal emotion recognition has achieved significant progress over recent years, the potential of rich synergic relationships across the modalities is not fully exploited. In this paper, we introduce Recursive Joint Cross-Modal Attention (RJCMA) to effectively capture both intra- and inter-modal relationships across audio, visual, and text modalities for dimensional emotion recognition. In particular, we compute the attention weights based on cross-correlation between the joint audio-visual-text feature representations and the feature representations of individual modalities to simultaneously capture intra- and inter-modal relationships across the modalities. The attended features of the individual modalities are again fed as input to the fusion model in a recursive mechanism to obtain more refined feature representations. We have also explored Temporal Convolutional Networks (TCNs) to improve the temporal modeling of the feature representations of individual modalities. Extensive experiments are conducted to evaluate the performance of the proposed fusion model on the challenging Affwild2 dataset. By effectively capturing the synergic intra- and inter-modal relationships across audio, visual, and text modalities, the proposed fusion model achieves a Concordance Correlation Coefficient (CCC) of 0.585 (0.542) and 0.674 (0.619) for valence and arousal, respectively, on the validation set (test set). This shows a significant improvement over the baseline of 0.240 (0.211) and 0.200 (0.191) for valence and arousal, respectively, in the validation set (test set), achieving second place in the valence-arousal challenge of the 6th Affective Behavior Analysis in-the-Wild (ABAW) competition. The code is available on GitHub: https://***/praveena2j/RJCMA.

关键词： Correlation coefficient Visualization Emotion recognition computer vision Computational modeling conferences Refining

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