... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision最新文献_第2页

When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. 当猪飞起来的时候合成场景和自然场景中的情境推理

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision Pub Date : 2021-10-01 Epub Date: 2022-02-28 DOI: 10.1109/iccv48922.2021.00032

Philipp Bomatter, Mengmi Zhang, Dimitar Karev, Spandan Madan, Claire Tseng, Gabriel Kreiman

{"title":"When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes.","authors":"Philipp Bomatter, Mengmi Zhang, Dimitar Karev, Spandan Madan, Claire Tseng, Gabriel Kreiman","doi":"10.1109/iccv48922.2021.00032","DOIUrl":"10.1109/iccv48922.2021.00032","url":null,"abstract":"Context is of fundamental importance to both human and machine vision; e.g., an object in the air is more likely to be an airplane than a pig. The rich notion of context incorporates several aspects including physics rules, statistical co-occurrences, and relative object sizes, among others. While previous work has focused on crowd-sourced out-of-context photographs from the web to study scene context, controlling the nature and extent of contextual violations has been a daunting task. Here we introduce a diverse, synthetic Out-of-Context Dataset (OCD) with fine-grained control over scene context. By leveraging a 3D simulation engine, we systematically control the gravity, object co-occurrences and relative sizes across 36 object categories in a virtual household environment. We conducted a series of experiments to gain insights into the impact of contextual cues on both human and machine vision using OCD. We conducted psychophysics experiments to establish a human benchmark for out-of-context recognition, and then compared it with state-of-the-art computer vision models to quantify the gap between the two. We propose a context-aware recognition transformer model, fusing object and contextual information via multi-head attention. Our model captures useful information for contextual reasoning, enabling human-level performance and better robustness in out-of-context conditions compared to baseline models across OCD and other out-of-context datasets. All source code and data are publicly available at https://github.com/kreimanlab/WhenPigsFlyContext.","PeriodicalId":72022,"journal":{"name":"... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision","volume":" ","pages":"255-264"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9432425/pdf/nihms-1831598.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40342711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-scanner Harmonization of Paired Neuroimaging Data via Structure Preserving Embedding Learning. 通过结构保持嵌入学习实现配对神经影像数据的多扫描仪协调

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision Pub Date : 2021-10-01 Epub Date: 2021-11-24 DOI: 10.1109/ICCVW54120.2021.00367

Mahbaneh Eshaghzadeh Torbati, Dana L Tudorascu, Davneet S Minhas, Pauline Maillard, Charles S DeCarli, Seong Jae Hwang

{"title":"Multi-scanner Harmonization of Paired Neuroimaging Data via Structure Preserving Embedding Learning.","authors":"Mahbaneh Eshaghzadeh Torbati, Dana L Tudorascu, Davneet S Minhas, Pauline Maillard, Charles S DeCarli, Seong Jae Hwang","doi":"10.1109/ICCVW54120.2021.00367","DOIUrl":"10.1109/ICCVW54120.2021.00367","url":null,"abstract":"Combining datasets from multiple sites/scanners has been becoming increasingly more prevalent in modern neuroimaging studies. Despite numerous benefits from the growth in sample size, substantial technical variability associated with site/scanner-related effects exists which may inadvertently bias subsequent downstream analyses. Such a challenge calls for a data harmonization procedure which reduces the scanner effects and allows the scans to be combined for pooled analyses. In this work, we present MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a multi-scanner harmonization framework. Unlike existing techniques, MISPEL does not assume a perfect coregistration across the scans, and the framework is naturally extendable to more than two scanners. Importantly, we incorporate our multi-scanner dataset where each subject is scanned on four different scanners. This unique paired dataset allows us to define and aim for an ideal harmonization (e.g., each subject with identical brain tissue volumes on all scanners). We extensively view scanner effects under varying metrics and demonstrate how MISPEL significantly improves them.","PeriodicalId":72022,"journal":{"name":"... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision","volume":" ","pages":"3277-3286"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8668020/pdf/nihms-1760021.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39728309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling. 联合半监督和主动学习用于具有成本效益标记的十亿像素病理图像分割。

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision Pub Date : 2021-10-01 DOI: 10.1109/iccvw54120.2021.00072

Zhengfeng Lai, Chao Wang, Luca Cerny Oliveira, Brittany N Dugger, Sen-Ching Cheung, Chen-Nee Chuah

{"title":"Joint Semi-supervised and Active Learning for Segmentation of Gigapixel Pathology Images with Cost-Effective Labeling.","authors":"Zhengfeng Lai, Chao Wang, Luca Cerny Oliveira, Brittany N Dugger, Sen-Ching Cheung, Chen-Nee Chuah","doi":"10.1109/iccvw54120.2021.00072","DOIUrl":"https://doi.org/10.1109/iccvw54120.2021.00072","url":null,"abstract":"The need for manual and detailed annotations limits the applicability of supervised deep learning algorithms in medical image analyses, specifically in the field of pathology. Semi-supervised learning (SSL) provides an effective way for leveraging unlabeled data to relieve the heavy reliance on the amount of labeled samples when training a model. Although SSL has shown good performance, the performance of recent state-of-the-art SSL methods on pathology images is still under study. The problem for selecting the most optimal data to label for SSL is not fully explored. To tackle this challenge, we propose a semi-supervised active learning framework with a region-based selection criterion. This framework iteratively selects regions for annotation query to quickly expand the diversity and volume of the labeled set. We evaluate our framework on a grey-matter/white-matter segmentation problem using gigapixel pathology images from autopsied human brain tissues. With only 0.1% regions labeled, our proposed algorithm can reach a competitive IoU score compared to fully-supervised learning and outperform the current state-of-the-art SSL by more than 10% of IoU score and DICE coefficient.","PeriodicalId":72022,"journal":{"name":"... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision","volume":"2021 ","pages":"591-600"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8972970/pdf/nihms-1788370.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10656111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Decision Explanation and Feature Importance for Invertible Networks. 可逆网络的决策解释与特征重要性。

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision Pub Date : 2019-10-01 Epub Date: 2020-03-05 DOI: 10.1109/iccvw.2019.00521

Juntang Zhuang, Nicha C Dvornek, Xiaoxiao Li, Junlin Yang, James S Duncan

{"title":"Decision Explanation and Feature Importance for Invertible Networks.","authors":"Juntang Zhuang, Nicha C Dvornek, Xiaoxiao Li, Junlin Yang, James S Duncan","doi":"10.1109/iccvw.2019.00521","DOIUrl":"https://doi.org/10.1109/iccvw.2019.00521","url":null,"abstract":"Deep neural networks are vulnerable to adversarial attacks and hard to interpret because of their black-box nature. The recently proposed invertible network is able to accurately reconstruct the inputs to a layer from its outputs, thus has the potential to unravel the black-box model. An invertible network classifier can be viewed as a two-stage model: (1) invertible transformation from input space to the feature space; (2) a linear classifier in the feature space. We can determine the decision boundary of a linear classifier in the feature space; since the transform is invertible, we can invert the decision boundary from the feature space to the input space. Furthermore, we propose to determine the projection of a data point onto the decision boundary, and define explanation as the difference between data and its projection. Finally, we propose to locally approximate a neural network with its first-order Taylor expansion, and define feature importance using a local linear model. We provide the implementation of our method: https://github.com/juntang-zhuang/explain_invertible.","PeriodicalId":72022,"journal":{"name":"... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision","volume":"2019 ","pages":"4235-4239"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/iccvw.2019.00521","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38462061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation. 利用解剖学一致性嵌入进行跨模态肝脏分割的领域诊断学习

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision Pub Date : 2019-10-01 Epub Date: 2020-03-05 DOI: 10.1109/iccvw.2019.00043

Junlin Yang, Nicha C Dvornek, Fan Zhang, Juntang Zhuang, Julius Chapiro, MingDe Lin, James S Duncan

{"title":"Domain-Agnostic Learning with Anatomy-Consistent Embedding for Cross-Modality Liver Segmentation.","authors":"Junlin Yang, Nicha C Dvornek, Fan Zhang, Juntang Zhuang, Julius Chapiro, MingDe Lin, James S Duncan","doi":"10.1109/iccvw.2019.00043","DOIUrl":"10.1109/iccvw.2019.00043","url":null,"abstract":"Domain Adaptation (DA) has the potential to greatly help the generalization of deep learning models. However, the current literature usually assumes to transfer the knowledge from the source domain to a specific known target domain. Domain Agnostic Learning (DAL) proposes a new task of transferring knowledge from the source domain to data from multiple heterogeneous target domains. In this work, we propose the Domain-Agnostic Learning framework with Anatomy-Consistent Embedding (DALACE) that works on both domain-transfer and task-transfer to learn a disentangled representation, aiming to not only be invariant to different modalities but also preserve anatomical structures for the DA and DAL tasks in cross-modality liver segmentation. We validated and compared our model with state-of-the-art methods, including CycleGAN, Task Driven Generative Adversarial Network (TD-GAN), and Domain Adaptation via Disentangled Representations (DADR). For the DA task, our DALACE model outperformed CycleGAN, TD-GAN, and DADR with DSC of 0.847 compared to 0.721, 0.793 and 0.806. For the DAL task, our model improved the performance with DSC of 0.794 from 0.522, 0.719 and 0.742 by CycleGAN, TD-GAN, and DADR. Further, we visualized the success of disentanglement, which added human interpretability of the learned meaningful representations. Through ablation analysis, we specifically showed the concrete benefits of disentanglement for downstream tasks and the role of supervision for better disentangled representation with segmentation consistency to be invariant to domains with the proposed Domain-Agnostic Module (DAM) and to preserve anatomical information with the proposed Anatomy-Preserving Module (APM).","PeriodicalId":72022,"journal":{"name":"... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision","volume":"2019 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8528125/pdf/nihms-1596812.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39539140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

nocaps: novel object captioning at scale Nocaps:大规模的新对象字幕

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision Pub Date : 2019-01-01 DOI: 10.1109/ICCV.2019.00904

Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, Peter Anderson

{"title":"nocaps: novel object captioning at scale","authors":"Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark Johnson, Dhruv Batra, Devi Parikh, Stefan Lee, Peter Anderson","doi":"10.1109/ICCV.2019.00904","DOIUrl":"https://doi.org/10.1109/ICCV.2019.00904","url":null,"abstract":"Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed ‘nocaps’, for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the Open Images validation and test sets. The associated training data consists of COCO image-caption pairs, plus Open Images image-level labels and object bounding boxes. Since Open Images contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work.","PeriodicalId":72022,"journal":{"name":"... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision","volume":"63 1","pages":"8947-8956"},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89398399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 249

Joint Scale-Spatial Correlation Tracking with Adaptive Rotation Estimation 自适应旋转估计的联合尺度-空间相关跟踪

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision Pub Date : 2015-12-07 DOI: 10.1109/ICCVW.2015.81

Mengdan Zhang, Junliang Xing, Jin Gao, Xinchu Shi, Qiang Wang, Weiming Hu

{"title":"Joint Scale-Spatial Correlation Tracking with Adaptive Rotation Estimation","authors":"Mengdan Zhang, Junliang Xing, Jin Gao, Xinchu Shi, Qiang Wang, Weiming Hu","doi":"10.1109/ICCVW.2015.81","DOIUrl":"https://doi.org/10.1109/ICCVW.2015.81","url":null,"abstract":"Boosted by large and standardized benchmark datasets, visual object tracking has made great progress in recent years and brought about many new trackers. Among these trackers, correlation filter based tracking schema exhibits impressive robustness and accuracy. In this work, we present a fully functional correlation filter based tracking algorithm which is able to simultaneously model target appearance changes from spatial displacements, scale variations, and rotation transformations. The proposed tracker first represents the exhaustive template searching in the joint scale and spatial space by a block-circulant matrix. Then, by transferring the target template from the Cartesian coordinate system to the Log-Polar coordinate system, the circulant structure is well preserved for the target even after whole orientation rotation. With these novel representation and transformation, object tracking is efficiently and effectively performed in the joint space with fast Fourier Transform. Experimental results on the VOT 2015 benchmark dataset demonstrate its superior performance over state-of-the-art tracking algorithms.","PeriodicalId":72022,"journal":{"name":"... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision","volume":"29 1","pages":"595-603"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77623493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 56

Aggregating Local Deep Features for Image Retrieval 基于局部深度特征的图像检索

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.150

Artem Babenko, V. Lempitsky

{"title":"Aggregating Local Deep Features for Image Retrieval","authors":"Artem Babenko, V. Lempitsky","doi":"10.1109/ICCV.2015.150","DOIUrl":"https://doi.org/10.1109/ICCV.2015.150","url":null,"abstract":"Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It also has been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregating methods developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptor. In this paper we investigate possible ways to aggregate local deep features to produce compact descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. In addition, we suggest a simple yet efficient query expansion scheme suitable for the proposed aggregation method. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.","PeriodicalId":72022,"journal":{"name":"... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision","volume":"3 1","pages":"1269-1277"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81353089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 82

Structural Kernel Learning for Large Scale Multiclass Object Co-detection 大规模多类目标协同检测的结构核学习

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision Pub Date : 2015-12-07 DOI: 10.1109/ICCV.2015.302

Zeeshan Hayder, Xuming He, M. Salzmann

{"title":"Structural Kernel Learning for Large Scale Multiclass Object Co-detection","authors":"Zeeshan Hayder, Xuming He, M. Salzmann","doi":"10.1109/ICCV.2015.302","DOIUrl":"https://doi.org/10.1109/ICCV.2015.302","url":null,"abstract":"Exploiting contextual relationships across images has recently proven key to improve object detection. The resulting object co-detection algorithms, however, fail to exploit the correlations between multiple classes and, for scalability reasons are limited to modeling object instance similarity with relatively low-dimensional hand-crafted features. Here, we address the problem of multiclass object co-detection for large scale datasets. To this end, we formulate co-detection as the joint multiclass labeling of object candidates obtained in a class-independent manner. To exploit the correlations between objects, we build a fully-connected CRF on the candidates, which explicitly incorporates both geometric layout relations across object classes and similarity relations across multiple images. We then introduce a structural boosting algorithm that lets us exploits rich, high-dimensional deep network features to learn object similarity within our fully-connected CRF. Our experiments on PASCAL VOC 2007 and 2012 evidences the benefits of our approach over object detection with RCNN, single-image CRF methods and state-of-the-art co-detection algorithms.","PeriodicalId":72022,"journal":{"name":"... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision","volume":"10 1","pages":"2632-2640"},"PeriodicalIF":0.0,"publicationDate":"2015-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80880384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Shape Index Descriptors Applied to Texture-Based Galaxy Analysis 形状索引描述符应用于基于纹理的星系分析

... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision Pub Date : 2013-12-01 DOI: 10.1109/ICCV.2013.303

K. S. Pedersen, Kristoffer Stensbo-Smidt, A. Zirm, C. Igel

{"title":"Shape Index Descriptors Applied to Texture-Based Galaxy Analysis","authors":"K. S. Pedersen, Kristoffer Stensbo-Smidt, A. Zirm, C. Igel","doi":"10.1109/ICCV.2013.303","DOIUrl":"https://doi.org/10.1109/ICCV.2013.303","url":null,"abstract":"A texture descriptor based on the shape index and the accompanying curvedness measure is proposed, and it is evaluated for the automated analysis of astronomical image data. A representative sample of images of low-red shift galaxies from the Sloan Digital Sky Survey (SDSS) serves as a test bed. The goal of applying texture descriptors to these data is to extract novel information about galaxies, information which is often lost in more traditional analysis. In this study, we build a regression model for predicting a spectroscopic quantity, the specific star-formation rate (sSFR). As texture features we consider multi-scale gradient orientation histograms as well as multi-scale shape index histograms, which lead to a new descriptor. Our results show that we can successfully predict spectroscopic quantities from the texture in optical multi-band images. We successfully recover the observed bi-modal distribution of galaxies into quiescent and star-forming. The state-of-the-art for predicting the sSFR is a color-based physical model. We significantly improve its accuracy by augmenting the model with texture information. This study is the first step towards enabling the quantification of physical galaxy properties from imaging data alone.","PeriodicalId":72022,"journal":{"name":"... IEEE International Conference on Computer Vision workshops. IEEE International Conference on Computer Vision","volume":"67 1","pages":"2440-2447"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75589941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19