iccv iccv2013 iccv2013-92 knowledge-graph by maker-knowledge-mining

92 iccv-2013-Corrected-Moment Illuminant Estimation


Source: pdf

Author: Graham D. Finlayson

Abstract: unkown-abstract

Reference: text


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore


similar papers computed by tfidf model

tfidf for this paper:

wordName wordTfidf (topN-words)

[('hkikj', 0.666), ('cm', 0.487), ('hij', 0.454), ('ll', 0.254), ('bc', 0.186), ('bi', 0.116)]

similar papers list:

simIndex simValue paperId paperTitle

same-paper 1 1.0 92 iccv-2013-Corrected-Moment Illuminant Estimation

Author: Graham D. Finlayson

Abstract: unkown-abstract

2 0.035939831 264 iccv-2013-Minimal Basis Facility Location for Subspace Segmentation

Author: Choon-Meng Lee, Loong-Fah Cheong

Abstract: In contrast to the current motion segmentation paradigm that assumes independence between the motion subspaces, we approach the motion segmentation problem by seeking the parsimonious basis set that can represent the data. Our formulation explicitly looks for the overlap between subspaces in order to achieve a minimal basis representation. This parsimonious basis set is important for the performance of our model selection scheme because the sharing of basis results in savings of model complexity cost. We propose the use of affinity propagation based method to determine the number of motion. The key lies in the incorporation of a global cost model into the factor graph, serving the role of model complexity. The introduction of this global cost model requires additional message update in the factor graph. We derive an efficient update for the new messages associated with this global cost model. An important step in the use of affinity propagation is the subspace hypotheses generation. We use the row-sparse convex proxy solution as an initialization strategy. We further encourage the selection of subspace hypotheses with shared basis by integrat- ing a discount scheme that lowers the factor graph facility cost based on shared basis. We verified the model selection and classification performance of our proposed method on both the original Hopkins 155 dataset and the more balanced Hopkins 380 dataset.

3 0.031546816 179 iccv-2013-From Subcategories to Visual Composites: A Multi-level Framework for Object Detection

Author: Tian Lan, Michalis Raptis, Leonid Sigal, Greg Mori

Abstract: The appearance of an object changes profoundly with pose, camera view and interactions of the object with other objects in the scene. This makes it challenging to learn detectors based on an object-level label (e.g., “car”). We postulate that having a richer set oflabelings (at different levels of granularity) for an object, including finer-grained subcategories, consistent in appearance and view, and higherorder composites – contextual groupings of objects consistent in their spatial layout and appearance, can significantly alleviate these problems. However, obtaining such a rich set of annotations, including annotation of an exponentially growing set of object groupings, is simply not feasible. We propose a weakly-supervised framework for object detection where we discover subcategories and the composites automatically with only traditional object-level category labels as input. To this end, we first propose an exemplar-SVM-based clustering approach, with latent SVM refinement, that discovers a variable length set of discriminative subcategories for each object class. We then develop a structured model for object detection that captures interactions among object subcategories and automatically discovers semantically meaningful and discriminatively relevant visual composites. We show that this model produces state-of-the-art performance on UIUC phrase object detection benchmark.

4 0.026842562 120 iccv-2013-Discriminative Label Propagation for Multi-object Tracking with Sporadic Appearance Features

Author: K.C. Amit Kumar, Christophe De_Vleeschouwer

Abstract: Given a set of plausible detections, detected at each time instant independently, we investigate how to associate them across time. This is done by propagating labels on a set of graphs that capture how the spatio-temporal and the appearance cues promote the assignment of identical or distinct labels to a pair of nodes. The graph construction is driven by the locally linear embedding (LLE) of either the spatio-temporal or the appearance features associated to the detections. Interestingly, the neighborhood of a node in each appearance graph is defined to include all nodes for which the appearance feature is available (except the ones that coexist at the same time). This allows to connect the nodes that share the same appearance even if they are temporally distant, which gives our framework the uncommon ability to exploit the appearance features that are available only sporadically along the sequence of detections. Once the graphs have been defined, the multi-object tracking is formulated as the problem of finding a label assignment that is consistent with the constraints captured by each of the graphs. This results into a difference of convex program that can be efficiently solved. Experiments are performed on a basketball and several well-known pedestrian datasets in order to validate the effectiveness of the proposed solution.

5 0.025004154 211 iccv-2013-Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks

Author: Mojtaba Seyedhosseini, Mehdi Sajjadi, Tolga Tasdizen

Abstract: Contextual information plays an important role in solving vision problems such as image segmentation. However, extracting contextual information and using it in an effective way remains a difficult problem. To address this challenge, we propose a multi-resolution contextual framework, called cascaded hierarchical model (CHM), which learns contextual information in a hierarchical framework for image segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. We repeat this procedure by cascading the hierarchical framework to improve the segmentation accuracy. Multiple classifiers are learned in the CHM; therefore, a fast and accurate classifier is required to make the training tractable. The classifier also needs to be robust against overfitting due to the large number of parameters learned during training. We introduce a novel classification scheme, called logistic dis- junctive normal networks (LDNN), which consists of one adaptive layer of feature detectors implemented by logistic sigmoid functions followed by two fixed layers of logical units that compute conjunctions and disjunctions, respectively. We demonstrate that LDNN outperforms state-of-theart classifiers and can be used in the CHM to improve object segmentation performance.

6 0.021366857 9 iccv-2013-A Flexible Scene Representation for 3D Reconstruction Using an RGB-D Camera

7 0.018217893 85 iccv-2013-Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach

8 0.016898027 104 iccv-2013-Decomposing Bag of Words Histograms

9 0.016631324 409 iccv-2013-Supervised Binary Hash Code Learning with Jensen Shannon Divergence

10 0.015424347 10 iccv-2013-A Framework for Shape Analysis via Hilbert Space Embedding

11 0.011947272 300 iccv-2013-Optical Flow via Locally Adaptive Fusion of Complementary Data Costs

12 0.011739621 130 iccv-2013-Dynamic Structured Model Selection

13 0.008815852 333 iccv-2013-Quantize and Conquer: A Dimensionality-Recursive Solution to Clustering, Vector Quantization, and Image Retrieval

14 0.0086585162 340 iccv-2013-Real-Time Articulated Hand Pose Estimation Using Semi-supervised Transductive Regression Forests

15 0.0081340047 121 iccv-2013-Discriminatively Trained Templates for 3D Object Detection: A Real Time Scalable Approach

16 0.0077005718 16 iccv-2013-A Generic Deformation Model for Dense Non-rigid Surface Registration: A Higher-Order MRF-Based Approach

17 0.0071439235 3 iccv-2013-3D Sub-query Expansion for Improving Sketch-Based Multi-view Image Retrieval

18 0.0069138808 72 iccv-2013-Characterizing Layouts of Outdoor Scenes Using Spatial Topic Processes

19 0.006823183 445 iccv-2013-Visual Reranking through Weakly Supervised Multi-graph Learning

20 0.0067325998 387 iccv-2013-Shape Anchors for Data-Driven Multi-view Reconstruction


similar papers computed by lsi model

lsi for this paper:

topicId topicWeight

[(0, 0.007), (1, -0.001), (2, -0.001), (3, -0.002), (4, -0.0), (5, 0.007), (6, -0.003), (7, -0.0), (8, 0.007), (9, -0.007), (10, -0.002), (11, 0.005), (12, -0.003), (13, -0.004), (14, -0.006), (15, -0.009), (16, -0.006), (17, 0.005), (18, 0.003), (19, 0.009), (20, 0.003), (21, -0.003), (22, 0.016), (23, 0.012), (24, -0.009), (25, -0.009), (26, 0.006), (27, 0.008), (28, -0.005), (29, -0.011), (30, -0.017), (31, 0.025), (32, -0.008), (33, -0.001), (34, -0.03), (35, 0.007), (36, -0.024), (37, -0.034), (38, 0.031), (39, 0.018), (40, 0.027), (41, 0.027), (42, -0.004), (43, 0.008), (44, -0.028), (45, 0.026), (46, 0.032), (47, -0.007), (48, -0.003), (49, 0.042)]

similar papers list:

simIndex simValue paperId paperTitle

same-paper 1 0.99818414 92 iccv-2013-Corrected-Moment Illuminant Estimation

Author: Graham D. Finlayson

Abstract: unkown-abstract

2 0.36044735 57 iccv-2013-BOLD Features to Detect Texture-less Objects

Author: Federico Tombari, Alessandro Franchi, Luigi Di_Stefano

Abstract: Object detection in images withstanding significant clutter and occlusion is still a challenging task whenever the object surface is characterized by poor informative content. We propose to tackle this problem by a compact and distinctive representation of groups of neighboring line segments aggregated over limited spatial supports and invariant to rotation, translation and scale changes. Peculiarly, our proposal allows for leveraging on the inherent strengths of descriptor-based approaches, i.e. robustness to occlusion and clutter and scalability with respect to the size of the model library, also when dealing with scarcely textured objects.

3 0.34524614 22 iccv-2013-A New Adaptive Segmental Matching Measure for Human Activity Recognition

Author: Shahriar Shariat, Vladimir Pavlovic

Abstract: The problem of human activity recognition is a central problem in many real-world applications. In this paper we propose a fast and effective segmental alignmentbased method that is able to classify activities and interactions in complex environments. We empirically show that such model is able to recover the alignment that leads to improved similarity measures within sequence classes and hence, raises the classification performance. We also apply a bounding technique on the histogram distances to reduce the computation of the otherwise exhaustive search.

4 0.30427656 442 iccv-2013-Video Segmentation by Tracking Many Figure-Ground Segments

Author: Fuxin Li, Taeyoung Kim, Ahmad Humayun, David Tsai, James M. Rehg

Abstract: We propose an unsupervised video segmentation approach by simultaneously tracking multiple holistic figureground segments. Segment tracks are initialized from a pool of segment proposals generated from a figure-ground segmentation algorithm. Then, online non-local appearance models are trained incrementally for each track using a multi-output regularized least squares formulation. By using the same set of training examples for all segment tracks, a computational trick allows us to track hundreds of segment tracks efficiently, as well as perform optimal online updates in closed-form. Besides, a new composite statistical inference approach is proposed for refining the obtained segment tracks, which breaks down the initial segment proposals and recombines for better ones by utilizing highorder statistic estimates from the appearance model and enforcing temporal consistency. For evaluating the algorithm, a dataset, SegTrack v2, is collected with about 1,000 frames with pixel-level annotations. The proposed framework outperforms state-of-the-art approaches in the dataset, show- ing its efficiency and robustness to challenges in different video sequences.

5 0.30289868 170 iccv-2013-Fingerspelling Recognition with Semi-Markov Conditional Random Fields

Author: Taehwan Kim, Greg Shakhnarovich, Karen Livescu

Abstract: Recognition of gesture sequences is in general a very difficult problem, but in certain domains the difficulty may be mitigated by exploiting the domain ’s “grammar”. One such grammatically constrained gesture sequence domain is sign language. In this paper we investigate the case of fingerspelling recognition, which can be very challenging due to the quick, small motions of the fingers. Most prior work on this task has assumed a closed vocabulary of fingerspelled words; here we study the more natural open-vocabulary case, where the only domain knowledge is the possible fingerspelled letters and statistics of their sequences. We develop a semi-Markov conditional model approach, where feature functions are defined over segments of video and their corresponding letter labels. We use classifiers of letters and linguistic handshape features, along with expected motion profiles, to define segmental feature functions. This approach improves letter error rate (Levenshtein distance between hypothesized and correct letter sequences) from 16.3% using a hidden Markov model baseline to 11.6% us- ing the proposed semi-Markov model.

6 0.29623336 250 iccv-2013-Lifting 3D Manhattan Lines from a Single Image

7 0.28859711 436 iccv-2013-Unsupervised Intrinsic Calibration from a Single Frame Using a "Plumb-Line" Approach

8 0.251553 346 iccv-2013-Rectangling Stereographic Projection for Wide-Angle Image Visualization

9 0.24286929 412 iccv-2013-Synergistic Clustering of Image and Segment Descriptors for Unsupervised Scene Understanding

10 0.22454336 181 iccv-2013-Frustratingly Easy NBNN Domain Adaptation

11 0.21638143 74 iccv-2013-Co-segmentation by Composition

12 0.21631585 375 iccv-2013-Scene Collaging: Analysis and Synthesis of Natural Images with Semantic Layers

13 0.21448328 279 iccv-2013-Multi-stage Contextual Deep Learning for Pedestrian Detection

14 0.21413067 127 iccv-2013-Dynamic Pooling for Complex Event Recognition

15 0.21111749 113 iccv-2013-Deterministic Fitting of Multiple Structures Using Iterative MaxFS with Inlier Scale Estimation

16 0.2099995 37 iccv-2013-Action Recognition and Localization by Hierarchical Space-Time Segments

17 0.20973825 241 iccv-2013-Learning Near-Optimal Cost-Sensitive Decision Policy for Object Detection

18 0.20453033 19 iccv-2013-A Learning-Based Approach to Reduce JPEG Artifacts in Image Matting

19 0.20370302 323 iccv-2013-Pose Estimation with Unknown Focal Length Using Points, Directions and Lines

20 0.1981001 155 iccv-2013-Facial Action Unit Event Detection by Cascade of Tasks


similar papers computed by lda model

lda for this paper:

topicId topicWeight

[(95, 0.687)]

similar papers list:

simIndex simValue paperId paperTitle

same-paper 1 1.0 92 iccv-2013-Corrected-Moment Illuminant Estimation

Author: Graham D. Finlayson

Abstract: unkown-abstract

2 0.37720412 25 iccv-2013-A Novel Earth Mover's Distance Methodology for Image Matching with Gaussian Mixture Models

Author: Peihua Li, Qilong Wang, Lei Zhang

Abstract: The similarity or distance measure between Gaussian mixture models (GMMs) plays a crucial role in contentbased image matching. Though the Earth Mover’s Distance (EMD) has shown its advantages in matching histogram features, its potentials in matching GMMs remain unclear and are not fully explored. To address this problem, we propose a novel EMD methodology for GMM matching. We ?rst present a sparse representation based EMD called SR-EMD by exploiting the sparse property of the underlying problem. SR-EMD is more ef?cient and robust than the conventional EMD. Second, we present two novel ground distances between component Gaussians based on the information geometry. The perspective from the Riemannian geometry distinguishes the proposed ground distances from the classical entropy- or divergence-based ones. Furthermore, motivated by the success of distance metric learning of vector data, we make the ?rst attempt to learn the EMD distance metrics between GMMs by using a simple yet effective supervised pair-wise based method. It can adapt the distance metrics between GMMs to speci?c classi?ca- tion tasks. The proposed method is evaluated on both simulated data and benchmark real databases and achieves very promising performance.

3 0.29966852 237 iccv-2013-Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes

Author: Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, Ryosuke Shibasaki

Abstract: Although graph matching is a fundamental problem in pattern recognition, and has drawn broad interest from many fields, the problem of learning graph matching has not received much attention. In this paper, we redefine the learning ofgraph matching as a model learningproblem. In addition to conventional training of matching parameters, our approach modifies the graph structure and attributes to generate a graphical model. In this way, the model learning is oriented toward both matching and recognition performance, and can proceed in an unsupervised1 fashion. Experiments demonstrate that our approach outperforms conventional methods for learning graph matching.

4 0.23861966 16 iccv-2013-A Generic Deformation Model for Dense Non-rigid Surface Registration: A Higher-Order MRF-Based Approach

Author: Yun Zeng, Chaohui Wang, Xianfeng Gu, Dimitris Samaras, Nikos Paragios

Abstract: We propose a novel approach for dense non-rigid 3D surface registration, which brings together Riemannian geometry and graphical models. To this end, we first introduce a generic deformation model, called Canonical Distortion Coefficients (CDCs), by characterizing the deformation of every point on a surface using the distortions along its two principle directions. This model subsumes the deformation groups commonly used in surface registration such as isometry and conformality, and is able to handle more complex deformations. We also derive its discrete counterpart which can be computed very efficiently in a closed form. Based on these, we introduce a higher-order Markov Random Field (MRF) model which seamlessly integrates our deformation model and a geometry/texture similarity metric. Then we jointly establish the optimal correspondences for all the points via maximum a posteriori (MAP) inference. Moreover, we develop a parallel optimization algorithm to efficiently perform the inference for the proposed higher-order MRF model. The resulting registration algorithm outperforms state-of-the-art methods in both dense non-rigid 3D surface registration and tracking.

5 0.23085743 263 iccv-2013-Measuring Flow Complexity in Videos

Author: Saad Ali

Abstract: In this paper a notion of flow complexity that measures the amount of interaction among objects is introduced and an approach to compute it directly from a video sequence is proposed. The approach employs particle trajectories as the input representation of motion and maps it into a ‘braid’ based representation. The mapping is based on the observation that 2D trajectories of particles take the form of a braid in space-time due to the intermingling among particles over time. As a result of this mapping, the problem of estimating the flow complexity from particle trajectories becomes the problem of estimating braid complexity, which in turn can be computed by measuring the topological entropy of a braid. For this purpose recently developed mathematical tools from braid theory are employed which allow rapid computation of topological entropy of braids. The approach is evaluated on a dataset consisting of open source videos depicting variations in terms of types of moving objects, scene layout, camera view angle, motion patterns, and object densities. The results show that the proposed approach is able to quantify the complexity of the flow, and at the same time provides useful insights about the sources of the complexity.

6 0.19510278 182 iccv-2013-GOSUS: Grassmannian Online Subspace Updates with Structured-Sparsity

7 0.15186028 111 iccv-2013-Detecting Dynamic Objects with Multi-view Background Subtraction

8 0.078756057 114 iccv-2013-Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution

9 0.075689778 257 iccv-2013-Log-Euclidean Kernels for Sparse Representation and Dictionary Learning

10 0.073744908 224 iccv-2013-Joint Optimization for Consistent Multiple Graph Matching

11 0.060132798 79 iccv-2013-Coherent Object Detection with 3D Geometric Context from a Single Image

12 0.055415489 183 iccv-2013-Geometric Registration Based on Distortion Estimation

13 0.050852053 164 iccv-2013-Fibonacci Exposure Bracketing for High Dynamic Range Imaging

14 0.049915709 347 iccv-2013-Recursive Estimation of the Stein Center of SPD Matrices and Its Applications

15 0.046265688 130 iccv-2013-Dynamic Structured Model Selection

16 0.041235078 433 iccv-2013-Understanding High-Level Semantics by Modeling Traffic Patterns

17 0.041025519 100 iccv-2013-Curvature-Aware Regularization on Riemannian Submanifolds

18 0.040824886 172 iccv-2013-Flattening Supervoxel Hierarchies by the Uniform Entropy Slice

19 0.035317581 272 iccv-2013-Modifying the Memorability of Face Photographs

20 0.034305867 397 iccv-2013-Space-Time Tradeoffs in Photo Sequencing