pwc-archive/papers-with-abstracts huggingface dataset:
Illumination and Reflectance Spectra Separation of a Hyperspectral Image Meets Low-Rank Matrix Factorization
Authors: Yinqiang Zheng, Imari Sato, Yoichi Sato
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Computational Efficiency

This paper addresses the illumination and reflectance spectra separation (IRSS) problem of a hyperspectral image captured under general spectral illumination. The huge amount of pixels in a hypersepctral image poses tremendous challenges on computational efficiency, yet in turn offers greater color variety that might be utilized to improve separation accuracy and relax the restrictive subspace illumination assumption in existing works. We show that this IRSS problem can be modeled into a low-rank matrix factorization problem, and prove that the separation is unique up to an unknown scale under the standard low-dimensionality assumption of reflectance. We also develop a scalable algorithm for this separation task that works in the presence of model error and image noise. Experiments on both synthetic data and real images have demonstrated that our separation results are sufficiently accurate, and can benefit some important applications, such as spectra relighting and illumination swapping.
24/7 Place Recognition by View Synthesis
Authors: Akihiko Torii, Relja Arandjelovic, Josef Sivic, Masatoshi Okutomi, Tomas Pajdla
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Visual Place Recognition

We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings built or destroyed. Such situations represent a major challenge for current large-scale place recognition methods. This work has the following three principal contributions. First, we demonstrate that matching across large changes in the scene appearance becomes much easier when both the query image and the database image depict the scene from approximately the same viewpoint. Second, based on this observation, we develop a new place recognition approach that combines (i) an efficient synthesis of novel views with (ii) a compact indexable image representation. Third, we introduce a new challenging dataset of 1,125 camera-phone query images of Tokyo that contain major changes in illumination (day, sunset, night) as well as structural changes in the scene. We demonstrate that the proposed approach significantly outperforms other large-scale place recognition techniques on this challenging data.
Book2Movie: Aligning Video Scenes With Book Chapters
Authors: Makarand Tapaswi, Martin Bauml, Rainer Stiefelhagen
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Video Alignment

Film adaptations of novels often visually display in a few shots what is described in many pages of the source novel. In this paper we present a new problem: to align book chapters with video scenes. Such an alignment facilitates finding differences between the adaptation and the original source, and also acts as a basis for deriving rich descriptions from the novel for the video clips. We propose an efficient method to compute an alignment between book chapters and video scenes using matching dialogs and character identities as cues. A major consideration is to allow the alignment to be non-sequential. Our suggested shortest path based approach deals with the non-sequential alignments and can be used to determine whether a video scene was part of the original book. We create a new data set involving two popular novel-to-film adaptations with widely varying properties and compare our method against other text-to-video alignment baselines. Using the alignment, we present a qualitative analysis of describing the video through rich narratives obtained from the novel.
3D Model-Based Continuous Emotion Recognition
Authors: Hui Chen, Jiangdong Li, Fengjun Zhang, Yang Li, Hongan Wang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Emotion Recognition, model

We propose a real-time 3D model-based method that continuously recognizes dimensional emotions from facial expressions in natural communications. In our method, 3D facial models are restored from 2D images, which provide crucial clues for the enhancement of robustness to overcome large changes including out-of-plane head rotations, fast head motions and partial facial occlusions. To accurately recognize the emotion, a novel random forest-based algorithm which simultaneously integrates two regressions for 3D facial tracking and continuous emotion estimation is constructed. Moreover, via the reconstructed 3D facial model, temporal information and user-independent emotion presentations are also taken into account through our image fusion process. The experimental results show that our algorithm can achieve state-of-the-art result with higher Pearson's correlation coefficient of continuous emotion recognition in real time.
Making Better Use of Edges via Perceptual Grouping
Authors: Yonggang Qi, Yi-Zhe Song, Tao Xiang, Honggang Zhang, Timothy Hospedales, Yi Li, Jun Guo
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Retrieval, Learning-To-Rank, Retrieval, Sketch-Based Image Retrieval

We propose a perceptual grouping framework that organizes image edges into meaningful structures and demonstrate its usefulness on various computer vision tasks. Our grouper formulates edge grouping as a graph partition problem, where a learning to rank method is developed to encode probabilities of candidate edge pairs. In particular, RankSVM is employed for the first time to combine multiple Gestalt principles as cue for edge grouping. Afterwards, an edge grouping based object proposal measure is introduced that yields proposals comparable to state-of-the-art alternatives. We further show how human-like sketches can be generated from edge groupings and consequently used to deliver state-of-the-art sketch-based image retrieval performance. Last but not least, we tackle the problem of freehand human sketch segmentation by utilizing the proposed grouper to cluster strokes into semantic object parts.
Real-Time Joint Estimation of Camera Orientation and Vanishing Points
Authors: Jeong-Kyun Lee, Kuk-Jin Yoon
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Management

The widely-used approach for estimating camera orientation is to use points at infinity, i.e., vanishing points (VPs). By enforcing the orthogonal constraint between the VPs, called the Manhattan world constraint, a drift-free camera orientation estimation can be achieved. However, in practical applications this approach suffers from many spurious parallel line segments or does not perform in non-Manhattan world scenes. To overcome these limitations, we propose a novel method that jointly estimates the VPs and camera orientation based on sequential Bayesian filtering. The proposed method does not require the Manhattan world assumption, and can perform a highly accurate estimation of camera orientation in real time. In addition, in order to enhance the robustness of the joint estimation, we propose a feature management technique that removes false positives of line clusters and classifies newly detected lines. We demonstrate the superiority of the proposed method through an extensive evaluation using synthetic and real datasets and comparison with other state-of-the-art methods.
Salient Object Detection via Bootstrap Learning
Authors: Na Tong, Huchuan Lu, Xiang Ruan, Ming-Hsuan Yang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

We propose a bootstrap learning algorithm for salient object detection in which both weak and strong models are exploited. First, a weak saliency map is constructed based on image priors to generate training samples for a strong model. Second, a strong classifier based on samples directly from an input image is learned to detect salient pixels. Results from multiscale saliency maps are integrated to further improve the detection performance. Extensive experiments on five benchmark datasets demonstrate that the proposed bootstrap learning algorithm performs favorably against the state-of-the-art saliency detection methods. Furthermore, we show that the proposed bootstrap learning approach can be easily applied to other bottom-up saliency models for significant improvement.
Data-Driven 3D Voxel Patterns for Object Category Recognition
Authors: Yu Xiang, Wongun Choi, Yuanqing Lin, Silvio Savarese
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Object, Object Recognition, Pose Estimation

Despite the great progress achieved in recognizing objects as 2D bounding boxes in images, it is still very challenging to detect occluded objects and estimate the 3D properties of multiple objects from a single image. In this paper, we propose a novel object representation, 3D Voxel Pattern (3DVP), that jointly encodes the key properties of objects including appearance, 3D shape, viewpoint, occlusion and truncation. We discover 3DVPs in a data-driven way, and train a bank of specialized detectors for a dictionary of 3DVPs. The 3DVP detectors are capable of detecting objects with specific visibility patterns and transferring the meta-data from the 3DVPs to the detected objects, such as 2D segmentation mask, 3D pose as well as occlusion or truncation boundaries. The transferred meta-data allows us to infer the occlusion relationship among objects, which in turn provides improved object recognition results. Experiments are conducted on the KITTI detection benchmark and the outdoor-scene dataset. We improve state-of-the-art results on car detection and pose estimation with notable margins (6% in difficult data of KITTI). We also verify the ability of our method in accurately segmenting objects from the background and localizing them in 3D.
Robust Image Alignment With Multiple Feature Descriptors and Matching-Guided Neighborhoods
Authors: Kuang-Jui Hsu, Yen-Yu Lin, Yung-Yu Chuang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6

This paper addresses two issues hindering the advances in accurate image alignment. First, the performance of descriptor-based approaches to image alignment relies on the chosen descriptor, but the optimal descriptor typically varies from image to image, or even pixel to pixel. Second, the neighborhood structure for smoothness enforcement is usually predefined before alignment. However, object boundaries are often better discovered during alignment. The proposed approach tackles the two issues by adaptive descriptor selection and dynamic neighborhood construction. Specifically, we associate each pixel to be aligned with an affine transformation, and integrate the learning of the pixel-specific transformations into image alignment. The transformations serve as the common domain for descriptor fusion, since the local consensus of each descriptor can be estimated by accessing the corresponding affine transformation. It allows us to pick the most plausible descriptor for aligning each pixel. On the other hand, more object-aware neighborhoods can be produced by referencing the consistency between the learned affine transformations of neighboring pixels. The promising results on popular image alignment benchmarks manifests the effectiveness of our approach.
Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A
Authors: Brendan F. Klare, Ben Klein, Emma Taborsky, Austin Blanton, Jordan Cheney, Kristen Allen, Patrick Grother, Alan Mah, Anil K. Jain
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Benchmarking, Face Detection, Face Recognition, Robust Face Recognition

Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search)  and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.
Depth From Shading, Defocus, and Correspondence Using Light-Field Angular Coherence
Authors: Michael W. Tao, Pratul P. Srinivasan, Jitendra Malik, Szymon Rusinkiewicz, Ravi Ramamoorthi
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Depth Estimation

 Light-field cameras are now used in consumer and industrial applications. Recent papers and products have demonstrated practical depth recovery algorithms from a passive single-shot capture. However, current light field capture devices have narrow baselines and constrained spatial resolution; therefore, the accuracy of depth recovery is limited, requiring heavy regularization and producing planar depths that do not resemble the actual geometry. Using shading information is essential to improve the shape estimation. We develop an improved technique for local shape estimation from defocus and correspondence cues, and show how shading can be used to further refine the depth.  Light-field cameras are able to capture both spatial and angular data, suitable for refocusing. By locally refocusing each spatial pixel to its respective estimated depth, we produce an all-in-focus image where all viewpoints converge onto a point in the scene. Therefore, the angular pixels have angular coherence, which exhibits three properties: photo consistency, depth consistency, and shading consistency. We propose a new framework that uses angular coherence to optimize depth and shading. The optimization framework estimates both general lighting in natural scenes and shading to improve depth regularization. Our method outperforms current state-of-the-art light-field depth estimation algorithms in multiple scenarios, including real images.
New Insights Into Laplacian Similarity Search
Authors: Xiao-Ming Wu, Zhenguo Li, Shih-Fu Chang
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Retrieval, Retrieval

Graph-based computer vision applications rely critically on similarity metrics which compute the pairwise similarity between any pair of vertices on graphs. This paper investigates the fundamental design of commonly used similarity metrics, and provides new insights to guide their use in practice. In particular, we introduce a family of similarity metrics in the form of (L+\alpha\Lambda)^{-1}, where L is the graph Laplacian, \Lambda is a positive diagonal matrix acting as a regularizer, and \alpha is a positive balancing factor. Such metrics respect graph topology when \alpha is small, and reproduce well-known metrics such as hitting times and the pseudo-inverse of graph Laplacian with different regularizer \Lambda.     This paper is the first to analyze the important impact of selecting \Lambda in retrieving the local cluster from a seed. We find that different \Lambda can lead to surprisingly complementary behaviors: \Lambda = D (degree matrix) can reliably extract the cluster of a query if it is sparser than surrounding clusters, while \Lambda = I (identity matrix) is preferred if it is denser than surrounding clusters. Since in practice there is no reliable way to determine the local density in order to select the right model, we propose a new design of \Lambda that automatically adapts to the local density. Experiments on image retrieval verify our theoretical arguments and confirm the benefit of the proposed metric. We expect the insights of our theory to provide guidelines for more applications in computer vision and other domains.
Feature-Independent Context Estimation for Automatic Image Annotation
Authors: Amara Tariq, Hassan Foroosh
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Retrieval, Retrieval, Tensor Decomposition

Automatic image annotation is a highly valuable tool for image search, retrieval and archival systems. In the absence of an annotation tool, such systems have to rely on either users' input or large amount of text on the webpage of the image, to acquire its textual description. Users may provide insufficient/noisy tags and all the text on the webpage may not be a description or an explanation of the accompanying image. Therefore, it is of extreme importance to develop efficient tools for automatic annotation of images with correct and sufficient tags. The context of the image plays a significant role in this process, along with the content of the image. A suitable quantification of the context of the image may reduce the semantic gap between visual features and appropriate textual description of the image. In this paper, we present an unsupervised feature-independent quantification of the context of the image through tensor decomposition. We incorporate the estimated context as prior knowledge in the process of automatic image annotation. Evaluation of the predicted annotations provides evidence of the effectiveness of our feature-independent context estimation method.
Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph
Authors: Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: General Classification

When data have a complex manifold structure or the characteristics of data evolve over time, it is unrealistic to expect a graph-based semi-supervised learning method to achieve flawless classification given a small number of initial annotations. To address this issue with minimal human interventions, we propose (i) a sample selection criterion used for \textit{active} query of informative samples by minimizing the expected prediction error, and (ii) an efficient {\it correction propagation} method that propagates human correction on selected samples over a {\it gradually-augmented graph} to unlabeled samples without rebuilding the affinity graph. Experimental results conducted on three real world datasets validate that our active sample selection and correction propagation algorithm quickly reaches high quality classification results with minimal human interventions.
Ranking and Retrieval of Image Sequences From Multiple Paragraph Queries
Authors: Gunhee Kim, Seungwhan Moon, Leonid Sigal
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Retrieval, Sentence

We propose a method to rank and retrieve image sequences from a natural language text query, consisting of multiple sentences or paragraphs. One of the method's key applications is to visualize visitors' text-only reviews on TRIPADVISOR or YELP, by automatically retrieving the most illustrative image sequences. While most previous work has dealt with the relations between a natural language sentence and an image or a video, our work extends to the relations between paragraphs and image sequences. Our approach leverages the vast user-generated resource of blog posts and photo streams on the Web. We use blog posts as text-image parallel training data that co-locate informative text with representative images that are carefully selected by users. We exploit large-scale photo streams to augment the image samples for retrieval. We design a latent structural SVM framework to learn the semantic relevance relations between text and image sequences. We present both quantitative and qualitative results on the newly created DISNEYLAND dataset.
Casual Stereoscopic Panorama Stitching
Authors: Fan Zhang, Feng Liu
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Image Stitching

This paper presents a method for stitching stereoscopic panoramas from stereo images casually taken using a stereo camera. This method addresses three challenges of stereoscopic image stitching: how to handle parallax, how to stitch the left- and right-view panorama consistently, and how to take care of disparity during stitching. This method addresses these challenges using a three-step approach. First, we employ a state-of-the-art stitching algorithm that handles parallax well to stitch the left views of input stereo images and create the left view of the final stereoscopic panorama. Second, we stitch the input disparity maps to obtain the target disparity map for the stereoscopic panorama by solving a Poisson's equation. This target disparity map is optimized such that there are no vertical disparities and the original perceived depth distribution is preserved. Finally, we warp the right views of the input stereo images and stitch them into the right view of the final stereoscopic panorama according to the target disparity map. The stitching of the right views is formulated as a labeling problem that is constrained by the stitching of the left views to make the left- and right-view panorama consistent to avoid retinal rivalry. Our experiments show that our method can effectively stitch casually taken stereo images and produce high-quality stereoscopic panoramas that deliver a pleasant stereoscopic 3D viewing experience.
Superpixel Meshes for Fast Edge-Preserving Surface Reconstruction
Authors: Andras Bodis-Szomoru, Hayko Riemenschneider, Luc van Gool
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Depth Estimation, Stereo Depth Estimation, Superpixels, Surface Reconstruction

Multi-View-Stereo (MVS) methods aim for the highest detail possible, however, such detail is often not required. In this work, we propose a novel surface reconstruction method based on image edges, superpixels and second-order smoothness constraints, producing meshes comparable to classic MVS surfaces in quality but orders of magnitudes faster. Our method performs per-view dense depth optimization directly over sparse 3D Ground Control Points (GCPs), hence, removing the need for view pairing, image rectification, and stereo depth estimation, and allowing for full per-image parallelization. We use Structure-from-Motion (SfM) points as GCPs, but the method is not specific to these, e.g.~LiDAR or RGB-D can also be used. The resulting meshes are compact and inherently edge-aligned with image gradients, enabling good-quality lightweight per-face flat renderings. Our experiments demonstrate on a variety of 3D datasets the superiority in speed and competitive surface quality.
Best-Buddies Similarity for Robust Template Matching
Authors: Tali Dekel, Shaul Oron, Michael Rubinstein, Shai Avidan, William T. Freeman
Date: 2015-06-01 00:00:00
Proceeding: CVPR 2015 6
Tasks: Template Matching

We propose a novel method for template matching in unconstrained environments. Its essence is the Best Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on a count of Best Buddies Pairs (BBPs)--pairs of points in which each one is the nearest neighbor of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset.