CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995629
Jan Heller, M. Havlena, A. Sugimoto, T. Pajdla
{"title":"Structure-from-motion based hand-eye calibration using L∞ minimization","authors":"Jan Heller, M. Havlena, A. Sugimoto, T. Pajdla","doi":"10.1109/CVPR.2011.5995629","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995629","url":null,"abstract":"This paper presents a novel method for so-called hand-eye calibration. Using a calibration target is not possible for many applications of hand-eye calibration. In such situations Structure-from-Motion approach of hand-eye calibration is commonly used to recover the camera poses up to scaling. The presented method takes advantage of recent results in the L∞-norm optimization using Second-Order Cone Programming (SOCP) to recover the correct scale. Further, the correctly scaled displacement of the hand-eye transformation is recovered solely from the image correspondences and robot measurements, and is guaranteed to be globally optimal with respect to the L∞-norm. The method is experimentally validated using both synthetic and real world datasets.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"414 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126696555","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}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995652
Bogdan Savchynskyy, Jörg H. Kappes, S. Schmidt, C. Schnörr
{"title":"A study of Nesterov's scheme for Lagrangian decomposition and MAP labeling","authors":"Bogdan Savchynskyy, Jörg H. Kappes, S. Schmidt, C. Schnörr","doi":"10.1109/CVPR.2011.5995652","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995652","url":null,"abstract":"We study the MAP-labeling problem for graphical models by optimizing a dual problem obtained by Lagrangian decomposition. In this paper, we focus specifically on Nes-terov's optimal first-order optimization scheme for non-smooth convex programs, that has been studied for a range of other problems in computer vision and machine learning in recent years. We show that in order to obtain an efficiently convergent iteration, this approach should be augmented with a dynamic estimation of a corresponding Lip-schitz constant, leading to a runtime complexity of O(1/∊) in terms of the desired precision ∊. Additionally, we devise a stopping criterion based on a duality gap as a sound basis for competitive comparison and show how to compute it efficiently. We evaluate our results using the publicly available Middlebury database and a set of computer generated graphical models that highlight specific aspects, along with other state-of-the-art methods for MAP-inference.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129165458","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}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995329
Behjat Siddiquie, R. Feris, L. Davis
{"title":"Image ranking and retrieval based on multi-attribute queries","authors":"Behjat Siddiquie, R. Feris, L. Davis","doi":"10.1109/CVPR.2011.5995329","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995329","url":null,"abstract":"We propose a novel approach for ranking and retrieval of images based on multi-attribute queries. Existing image retrieval methods train separate classifiers for each word and heuristically combine their outputs for retrieving multiword queries. Moreover, these approaches also ignore the interdependencies among the query terms. In contrast, we propose a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes. Given a multi-attribute query, we also utilize other attributes in the vocabulary which are not present in the query, for ranking/retrieval. Furthermore, we integrate ranking and retrieval within the same formulation, by posing them as structured prediction problems. Extensive experimental evaluation on the Labeled Faces in the Wild(LFW), FaceTracer and PASCAL VOC datasets show that our approach significantly outperforms several state-of-the-art ranking and retrieval methods.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123806690","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}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995601
Ondřej Chum, Andrej Mikulík, Michal Perdoch, Jiri Matas
{"title":"Total recall II: Query expansion revisited","authors":"Ondřej Chum, Andrej Mikulík, Michal Perdoch, Jiri Matas","doi":"10.1109/CVPR.2011.5995601","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995601","url":null,"abstract":"Most effective particular object and image retrieval approaches are based on the bag-of-words (BoW) model. All state-of-the-art retrieval results have been achieved by methods that include a query expansion that brings a significant boost in performance. We introduce three extensions to automatic query expansion: (i) a method capable of preventing tf-idf failure caused by the presence of sets of correlated features (confusers), (ii) an improved spatial verification and re-ranking step that incrementally builds a statistical model of the query object and (iii) we learn relevant spatial context to boost retrieval performance. The three improvements of query expansion were evaluated on standard Paris and Oxford datasets according to a standard protocol, and state-of-the-art results were achieved.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114232289","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}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995719
Liefeng Bo, Kevin Lai, Xiaofeng Ren, D. Fox
{"title":"Object recognition with hierarchical kernel descriptors","authors":"Liefeng Bo, Kevin Lai, Xiaofeng Ren, D. Fox","doi":"10.1109/CVPR.2011.5995719","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995719","url":null,"abstract":"Kernel descriptors [1] provide a unified way to generate rich visual feature sets by turning pixel attributes into patch-level features, and yield impressive results on many object recognition tasks. However, best results with kernel descriptors are achieved using efficient match kernels in conjunction with nonlinear SVMs, which makes it impractical for large-scale problems. In this paper, we propose hierarchical kernel descriptors that apply kernel descriptors recursively to form image-level features and thus provide a conceptually simple and consistent way to generate image-level features from pixel attributes. More importantly, hierarchical kernel descriptors allow linear SVMs to yield state-of-the-art accuracy while being scalable to large datasets. They can also be naturally extended to extract features over depth images. We evaluate hierarchical kernel descriptors both on the CIFAR10 dataset and the new RGB-D Object Dataset consisting of segmented RGB and depth images of 300 everyday objects.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121670722","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}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995598
Weishi Zheng, S. Gong, T. Xiang
{"title":"Person re-identification by probabilistic relative distance comparison","authors":"Weishi Zheng, S. Gong, T. Xiang","doi":"10.1109/CVPR.2011.5995598","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995598","url":null,"abstract":"Matching people across non-overlapping camera views, known as person re-identification, is challenging due to the lack of spatial and temporal constraints and large visual appearance changes caused by variations in view angle, lighting, background clutter and occlusion. To address these challenges, most previous approaches aim to extract visual features that are both distinctive and stable under appearance changes. However, most visual features and their combinations under realistic conditions are neither stable nor distinctive thus should not be used indiscriminately. In this paper, we propose to formulate person re-identification as a distance learning problem, which aims to learn the optimal distance that can maximises matching accuracy regardless the choice of representation. To that end, we introduce a novel Probabilistic Relative Distance Comparison (PRDC) model, which differs from most existing distance learning methods in that, rather than minimising intra-class variation whilst maximising intra-class variation, it aims to maximise the probability of a pair of true match having a smaller distance than that of a wrong match pair. This makes our model more tolerant to appearance changes and less susceptible to model over-fitting. Extensive experiments are carried out to demonstrate that 1) by formulating the person re-identification problem as a distance learning problem, notable improvement on matching accuracy can be obtained against conventional person re-identification techniques, which is particularly significant when the training sample size is small; and 2) our PRDC outperforms not only existing distance learning methods but also alternative learning methods based on boosting and learning to rank.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124091759","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}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995375
N. Komodakis
{"title":"Efficient training for pairwise or higher order CRFs via dual decomposition","authors":"N. Komodakis","doi":"10.1109/CVPR.2011.5995375","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995375","url":null,"abstract":"We present a very general algorithmic framework for structured prediction learning that is able to efficiently handle both pairwise and higher-order discrete MRFs/CRFs1. It relies on a dual decomposition approach that has been recently proposed for MRF optimization. By properly combining this approach with a max-margin method, our framework manages to reduce the training of a complex high-order MRF to the parallel training of a series of simple slave MRFs that are much easier to handle. This leads to an extremely efficient and general learning scheme. Furthermore, the proposed framework can yield learning algorithms of increasing accuracy since it naturally allows a hierarchy of convex relaxations to be used for MRF inference within a max-margin learning approach. It also offers extreme flexibility and can be easily adapted to take advantage of any special structure of a given class of MRFs. Experimental results demonstrate the great effectiveness of our method.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127666214","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}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995433
Junhong Gao, Seon Joo Kim, M. S. Brown
{"title":"Constructing image panoramas using dual-homography warping","authors":"Junhong Gao, Seon Joo Kim, M. S. Brown","doi":"10.1109/CVPR.2011.5995433","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995433","url":null,"abstract":"This paper describes a method to construct seamless image mosaics of a panoramic scene containing two predominate planes: a distant back plane and a ground plane that sweeps out from the camera's location. While this type of panorama can be stitched when the camera is carefully rotated about its optical center, such ideal scene capture is hard to perform correctly. Existing techniques use a single homography per image to perform alignment followed by seam cutting or image blending to hide inevitable alignments artifacts. In this paper, we demonstrate how to use two homographies per image to produce a more seamless image. Specifically, our approach blends the homographies in the alignment procedure to perform a nonlinear warping. Once the images are geometrically stitched, they are further processed to blend seams and reduce curvilinear visual artifacts due to the nonlinear warping. As demonstrated in our paper, our procedure is able to produce results for this type of scene where current state-of-the-art techniques fail.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125591100","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}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995545
Sudheendra Vijayanarasimhan, K. Grauman
{"title":"Efficient region search for object detection","authors":"Sudheendra Vijayanarasimhan, K. Grauman","doi":"10.1109/CVPR.2011.5995545","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995545","url":null,"abstract":"We propose a branch-and-cut strategy for efficient region-based object detection. Given an oversegmented image, our method determines the subset of spatially contiguous regions whose collective features will maximize a classifier's score. We formulate the objective as an instance of the prize-collecting Steiner tree problem, and show that for a family of additive classifiers this enables fast search for the optimal object region via a branch-and-cut algorithm. Unlike existing branch-and-bounddetection methods designed for bounding boxes, our approach allows scoring of irregular shapes — which is especially critical for objects that do not conform to a rectangular window. We provide results on three challenging object detection datasets, and demonstrate the advantage of rapidly seeking best-scoring regions rather than subwindow rectangles.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128155624","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}
CVPR 2011Pub Date : 2011-06-20DOI: 10.1109/CVPR.2011.5995639
Y. Kuo, Hsuan-Tien Lin, Wen-Huang Cheng, Yi-Hsuan Yang, Winston H. Hsu
{"title":"Unsupervised auxiliary visual words discovery for large-scale image object retrieval","authors":"Y. Kuo, Hsuan-Tien Lin, Wen-Huang Cheng, Yi-Hsuan Yang, Winston H. Hsu","doi":"10.1109/CVPR.2011.5995639","DOIUrl":"https://doi.org/10.1109/CVPR.2011.5995639","url":null,"abstract":"Image object retrieval–locating image occurrences of specific objects in large-scale image collections–is essential for manipulating the sheer amount of photos. Current solutions, mostly based on bags-of-words model, suffer from low recall rate and do not resist noises caused by the changes in lighting, viewpoints, and even occlusions. We propose to augment each image with auxiliary visual words (AVWs), semantically relevant to the search targets. The AVWs are automatically discovered by feature propagation and selection in textual and visual image graphs in an unsupervised manner. We investigate variant optimization methods for effectiveness and scalability in large-scale image collections. Experimenting in the large-scale consumer photos, we found that the the proposed method significantly improves the traditional bag-of-words (111% relatively). Meanwhile, the selection process can also notably reduce the number of features (to 1.4%) and can further facilitate indexing in large-scale image object retrieval.","PeriodicalId":445398,"journal":{"name":"CVPR 2011","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121733062","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}
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