IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision最新文献_第8页

Hierarchical representation of videos with spatio-temporal fibers 具有时空纤维的视频分层表示

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836064

Ratnesh Kumar, G. Charpiat, M. Thonnat

引用次数: 2

Furniture-geek: Understanding fine-grained furniture attributes from freely associated text and tags 家具极客:从自由关联的文本和标签中理解细粒度家具属性

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836083

Vicente Ordonez, V. Jagadeesh, Wei Di, Anurag Bhardwaj, Robinson Piramuthu

{"title":"Furniture-geek: Understanding fine-grained furniture attributes from freely associated text and tags","authors":"Vicente Ordonez, V. Jagadeesh, Wei Di, Anurag Bhardwaj, Robinson Piramuthu","doi":"10.1109/WACV.2014.6836083","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836083","url":null,"abstract":"As the amount of user generated content on the internet grows, it becomes ever more important to come up with vision systems that learn directly from weakly annotated and noisy data. We leverage a large scale collection of user generated content comprising of images, tags and title/captions of furniture inventory from an e-commerce website to discover and categorize learnable visual attributes. Furniture categories have long been the quintessential example of why computer vision is hard, and we make one of the first attempts to understand them through a large scale weakly annotated dataset. We focus on a handful of furniture categories that are associated with a large number of fine-grained attributes. We propose a set of localized feature representations built on top of state-of-the-art computer vision representations originally designed for fine-grained object categorization. We report a thorough empirical characterization on the visual identifiability of various fine-grained attributes using these representations and show encouraging results on finding iconic images and on multi-attribute prediction.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"11 1","pages":"317-324"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84157800","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: 10

Data-driven exemplar model selection 数据驱动的范例模型选择

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836080

Ishan Misra, Abhinav Shrivastava, M. Hebert

{"title":"Data-driven exemplar model selection","authors":"Ishan Misra, Abhinav Shrivastava, M. Hebert","doi":"10.1109/WACV.2014.6836080","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836080","url":null,"abstract":"We consider the problem of discovering discriminative exemplars suitable for object detection. Due to the diversity in appearance in real world objects, an object detector must capture variations in scale, viewpoint, illumination etc. The current approaches do this by using mixtures of models, where each mixture is designed to capture one (or a few) axis of variation. Current methods usually rely on heuristics to capture these variations; however, it is unclear which axes of variation exist and are relevant to a particular task. Another issue is the requirement of a large set of training images to capture such variations. Current methods do not scale to large training sets either because of training time complexity [31] or test time complexity [26]. In this work, we explore the idea of compactly capturing task-appropriate variation from the data itself. We propose a two stage data-driven process, which selects and learns a compact set of exemplar models for object detection. These selected models have an inherent ranking, which can be used for anytime/budgeted detection scenarios. Another benefit of our approach (beyond the computational speedup) is that the selected set of exemplar models performs better than the entire set.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"10 1","pages":"339-346"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84218738","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: 23

Rotation estimation from cloud tracking 从云跟踪估计旋转

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836006

Sangwoo Cho, Enrique Dunn, Jan-Michael Frahm

{"title":"Rotation estimation from cloud tracking","authors":"Sangwoo Cho, Enrique Dunn, Jan-Michael Frahm","doi":"10.1109/WACV.2014.6836006","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836006","url":null,"abstract":"We address the problem of online relative orientation estimation from streaming video captured by a sky-facing camera on a mobile device. Namely, we rely on the detection and tracking of visual features attained from cloud structures. Our proposed method achieves robust and efficient operation by combining realtime visual odometry modules, learning based feature classification, and Kalman filtering within a robustness-driven data management framework, while achieving framerate processing on a mobile device. The relatively large 3D distance between the camera and the observed cloud features is leveraged to simplify our processing pipeline. First, as an efficiency driven optimization, we adopt a homography based motion model and focus on estimating relative rotations across adjacent keyframes. To this end, we rely on efficient feature extraction, KLT tracking, and RANSAC based model fitting. Second, to ensure the validity of our simplified motion model, we segregate detected cloud features from scene features through SVM classification. Finally, to make tracking more robust, we employ predictive Kalman filtering to enable feature persistence through temporary occlusions and manage feature spatial distribution to foster tracking robustness. Results exemplify the accuracy and robustness of the proposed approach and highlight its potential as a passive orientation sensor.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"96 1","pages":"917-924"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85886402","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: 2

Small Hand-held Object Recognition Test (SHORT) 小型手持物体识别测试(SHORT)

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836057

Jose Rivera-Rubio, Saad Idrees, I. Alexiou, Lucas Hadjilucas, A. Bharath

{"title":"Small Hand-held Object Recognition Test (SHORT)","authors":"Jose Rivera-Rubio, Saad Idrees, I. Alexiou, Lucas Hadjilucas, A. Bharath","doi":"10.1109/WACV.2014.6836057","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836057","url":null,"abstract":"The ubiquity of smartphones with high quality cameras and fast network connections will spawn many new applications. One of these is visual object recognition, an emerging smartphone feature which could play roles in high-street shopping, price comparisons and similar uses. There are also potential roles for such technology in assistive applications, such as for people who have visual impairment. We introduce the Small Hand-held Object Recognition Test (SHORT), a new dataset that aims to benchmark the performance of algorithms for recognising hand-held objects from either snapshots or videos acquired using hand-held or wearable cameras. We show that SHORT provides a set of images and ground truth that help assess the many factors that affect recognition performance. SHORT is designed to be focused on the assistive systems context, though it can provide useful information on more general aspects of recognition performance for hand-held objects. We describe the present state of the dataset, comprised of a small set of high quality training images and a large set of nearly 135,000 smartphone-captured test images of 30 grocery products. In this version, SHORT addresses another context not covered by traditional datasets, in which high quality catalogue images are being compared with variable quality user-captured images; this makes the matching more challenging in SHORT than other datasets. Images of similar quality are often not present in “database” and “query” datasets, a situation being increasingly encountered in commercial applications. Finally, we compare the results of popular object recognition algorithms of different levels of complexity when tested against SHORT and discuss the research challenges arising from the particularities of visual object recognition from objects that are being held by users.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"9 1","pages":"524-531"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88417994","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: 13

Is my new tracker really better than yours? 我的新追踪器真的比你的好吗?

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836055

Luka Cehovin, M. Kristan, A. Leonardis

引用次数: 93

Offline learning of prototypical negatives for efficient online Exemplar SVM 高效在线样例支持向量机的原型否定离线学习

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836075

Masato Takami, Peter Bell, B. Ommer

{"title":"Offline learning of prototypical negatives for efficient online Exemplar SVM","authors":"Masato Takami, Peter Bell, B. Ommer","doi":"10.1109/WACV.2014.6836075","DOIUrl":"https://doi.org/10.1109/WACV.2014.6836075","url":null,"abstract":"Online searches in big image databases require sufficient results in feasible time. Digitization campaigns have simplified the access to a huge number of images in the field of art history, which can be analyzed by detecting duplicates and similar objects in the dataset. A high recall is essential for the evaluation and therefore the search method has to be robust against minor changes due to smearing or aging effects of the documents. At the same time the computational time has to be short to allow a practical use of the online search. By using an Exemplar SVM based classifier [12] a high recall can be achieved, but the mining of negatives and the multiple rounds of retraining for every search makes the method too time-consuming. An even bigger problem is that by allowing arbitrary query regions, it is not possible to provide a training set, which would be necessary to create a classifier. To solve this, we created a pool of general negatives offline in advance, which can be used by any arbitrary input in the online search step and requires only one short training round without the time-consuming mining. In a second step, this classifier is improved by using positive detections in an additional training round. This results in a classifier for the online search in unlabeled datasets, which provides high recall in short calculation time respectively.","PeriodicalId":73325,"journal":{"name":"IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision","volume":"81 1","pages":"377-384"},"PeriodicalIF":0.0,"publicationDate":"2014-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77308573","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: 2

“Important stuff, everywhere!” Activity recognition with salient proto-objects as context “重要的东西，到处都是!”以显著原对象为背景的活动识别

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836041

L. Rybok, Boris Schauerte, Ziad Al-Halah, R. Stiefelhagen

引用次数: 21

Object co-labeling in multiple images 在多个图像中进行对象共标记

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836031

Xi Chen, Arpit Jain, L. Davis

引用次数: 6

Multi class boosted random ferns for adapting a generic object detector to a specific video 多类增强随机蕨类植物适应一个通用的对象检测器到一个特定的视频

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision Pub Date : 2014-03-24 DOI: 10.1109/WACV.2014.6836028

Pramod Sharma, R. Nevatia

引用次数: 6