pwc-archive/papers-with-abstracts huggingface dataset:
Conformal and Low-Rank Sparse Representation for Image Restoration
Authors: Jianwei Li, Xiaowu Chen, Dongqing Zou, Bo Gao, Wei Teng
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Dictionary Learning, Image Restoration

Obtaining an appropriate dictionary is the key point when sparse representation is applied to computer vision or image processing problems such as image restoration. It is expected that preserving data structure during sparse coding and dictionary learning can enhance the recovery performance. However, many existing dictionary learning methods handle training samples individually, while missing relationships between samples, which result in dictionaries with redundant atoms but poor representation ability. In this paper, we propose a novel sparse representation approach called conformal and low-rank sparse representation (CLRSR) for image restoration problems. To achieve a more compact and representative dictionary, conformal property is introduced by preserving the angles of local geometry formed by neighboring samples in the feature space. Furthermore, imposing low-rank constraint on the coefficient matrix can lead more faithful subspaces and capture the global structure of data. We apply our CLRSR model to several image restoration tasks to demonstrate the effectiveness.
Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising
Authors: Jun Xu, Lei Zhang, WangMeng Zuo, David Zhang, Xiangchu Feng
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Denoising, Image Denoising

Patch based image modeling has achieved a great success in low level vision such as image denoising. In particular, the use of image nonlocal self-similarity (NSS) prior, which refers to the fact that a local patch often has many nonlocal similar patches to it across the image, has significantly enhanced the denoising performance. However, in most existing methods only the NSS of input degraded image is exploited, while how to utilize the NSS of clean natural images is still an open problem. In this paper, we propose a patch group (PG) based NSS prior learning scheme to learn explicit NSS models from natural images for high performance denoising. PGs are extracted from training images by putting nonlocal similar patches into groups, and a PG based Gaussian Mixture Model (PG-GMM) learning algorithm is developed to learn the NSS prior. We demonstrate that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods.
Automatic Thumbnail Generation Based on Visual Representativeness and Foreground Recognizability
Authors: Jingwei Huang, Huarong Chen, Bin Wang, Stephen Lin
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

We present an automatic thumbnail generation technique based on two essential considerations: how well they visually represent the original photograph, and how well the foreground can be recognized after the cropping and downsizing steps of thumbnailing. These factors, while important for the image indexing purpose of thumbnails, have largely been ignored in previous methods, which instead are designed to highlight salient content while disregarding the effects of downsizing. We propose a set of image features for modeling these two considerations of thumbnails, and learn how to balance their relative effects on thumbnail generation through training on image pairs composed of photographs and their corresponding thumbnails created by an expert photographer. Experiments show the effectiveness of this approach on a variety of images, as well as its advantages over related techniques.
SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks
Authors: Xun Huang, Chengyao Shen, Xavier Boix, Qi Zhao
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object Recognition, Saliency Prediction

Saliency in Context (SALICON) is an ongoing effort that aims at understanding and predicting visual attention. Conventional saliency models typically rely on low-level image statistics to predict human fixations. While these models perform significantly better than chance, there is still a large gap between model prediction and human behavior. This gap is largely due to the limited capability of models in predicting eye fixations with strong semantic content, the so-called semantic gap. This paper presents a focused study to narrow the semantic gap with an architecture based on Deep Neural Network (DNN). It leverages the representational power of high-level semantics encoded in DNNs pretrained for object recognition. Two key components are fine-tuning the DNNs fully convolutionally with an objective function based on the saliency evaluation metrics, and integrating information at different image scales. We compare our method with 14 saliency models on 6 public eye tracking benchmark datasets. Results demonstrate that our DNNs can automatically learn features particularly for saliency prediction that surpass by a big margin the state-of-the-art. In addition, our model ranks top to date under all seven metrics on the MIT300 challenge set.
A Novel Sparsity Measure for Tensor Recovery
Authors: Qian Zhao, Deyu Meng, Xu Kong, Qi Xie, Wenfei Cao, Yao Wang, Zongben Xu
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

In this paper, we propose a new sparsity regularizer for measuring the low-rank structure underneath a tensor. The proposed sparsity measure has a natural physical meaning which is intrinsically the size of the fundamental Kronecker basis to express the tensor. By embedding the sparsity measure into the tensor completion and tensor robust PCA frameworks, we formulate new models to enhance their capability in tensor recovery. Through introducing relaxation forms of the proposed sparsity measure, we also adopt the alternating direction method of multipliers (ADMM) for solving the proposed models. Experiments implemented on synthetic and multispectral image data sets substantiate the effectiveness of the proposed methods.
Oriented Object Proposals
Authors: Shengfeng He, Rynson W. H. Lau
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object

In this paper, we propose a new approach to generate oriented object proposals (OOPs) to reduce the detection error caused by various orientations of the object. To this end, we propose to efficiently locate object regions according to pixelwise object probability, rather than measuring the objectness from a set of sampled windows. We formulate the proposal generation problem as a generative probabilistic model such that object proposals of different shapes (i.e., sizes and orientations) can be produced by locating the local maximum likelihoods. The new approach has three main advantages. First, it helps the object detector handle objects of different orientations. Second, as the shapes of the proposals may vary to fit the objects, the resulting proposals are tighter than the sampling windows with fixed sizes. Third, it avoids massive window sampling, and thereby reducing the number of proposals while maintaining a high recall. Experiments on the PASCAL VOC 2007 dataset show that the proposed OOP outperforms the state-of-the-art fast methods. Further experiments show that the rotation invariant property helps a class-specific object detector achieve better performance than the state-of-the-art proposal generation methods in either object rotation scenarios or general scenarios. Generating OOPs is very fast and takes only 0.5s per image.
Learning Nonlinear Spectral Filters for Color Image Reconstruction
Authors: Michael Moeller, Julia Diebold, Guy Gilboa, Daniel Cremers
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Denoising, Image Denoising, Image Reconstruction

This paper presents the idea of learning optimal filters for color image reconstruction based on a novel concept of nonlinear spectral image decompositions recently proposed by Guy Gilboa. We use a multiscale image decomposition approach based on total variation regularization and Bregman iterations to represent the input data as the sum of image layers containing features at different scales. Filtered images can be obtained by weighted linear combinations of the different frequency layers. We introduce the idea of learning optimal filters for the task of image denoising, and propose the idea of mixing high frequency components of different color channels. Our numerical experiments demonstrate that learning the optimal weights can significantly improve the results in comparison to the standard variational approach, and achieves state-of-the-art image denoising results.
Beyond White: Ground Truth Colors for Color Constancy Correction
Authors: Dongliang Cheng, Brian Price, Scott Cohen, Michael S. Brown
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Color Constancy

A limitation in color constancy research is the inability to establish ground truth colors for evaluating corrected images. Many existing datasets contain images of scenes with a color chart included; however, only the chart's neutral colors (grayscale patches) are used to provide the ground truth for illumination estimation and correction.  This is because the corrected neutral colors are known to lie along the achromatic line in the camera's color space (i.e. R=G=B) ; the correct RGB values of the other color patches are not known.  As a result, most methods estimate a 3*3 diagonal matrix that ensures only the neutral colors are correct.  In this paper, we describe how to overcome this limitation.   Specifically, we show that under certain illuminations, a diagonal 3*3 matrix is capable of correcting not only neutral colors, but all the colors in a scene.   This finding allows us to find the ground truth RGB values for the color chart in the camera's color space. We show how to use this information to correct all the images in existing datasets to have correct colors.  Working from these new color corrected datasets, we describe how to modify existing color constancy algorithms to perform better image correction.
RGB-Guided Hyperspectral Image Upsampling
Authors: Hyeokhyen Kwon, Yu-Wing Tai
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Hyperspectral imaging usually lack of spatial resolution due to limitations of hardware design of imaging sensors. On the contrary, latest imaging sensors capture a RGB image with resolution of multiple times larger than a hyperspectral image. In this paper, we present an algorithm to enhance and upsample the resolution of hyperspectral images. Our algorithm consists of two stages: spatial upsampling stage and spectrum substitution stage. The spatial upsampling stage is guided by a high resolution RGB image of the same scene, and the spectrum substitution stage utilizes sparse coding to locally refine the upsampled hyperspectral image through dictionary substitution. Experiments show that our algorithm is highly effective and has outperformed state-of-the-art matrix factorization based approaches.
Projection Onto the Manifold of Elongated Structures for Accurate Extraction
Authors: Amos Sironi, Vincent Lepetit, Pascal Fua
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

Detection of elongated structures in 2D images and 3D image stacks is a critical prerequisite  in many  applications and  Machine Learning-based  approaches have recently been  shown to  deliver superior  performance.  However,  these methods essentially classify individual locations and do not explicitly model the strong relationship  that exists  between  neighboring ones.   As  a result,  isolated erroneous responses, discontinuities, and topological  errors are present in the resulting score maps.  We solve this  problem by projecting patches  of the score map  to their nearest neighbors in  a set  of ground  truth training  patches.  Our  algorithm induces global spatial consistency on the classifier  score map and returns results that are  provably geometrically consistent.   We apply  our  algorithm to  challenging datasets  in four  different domains  and show  that  it compares favorably to  state-of-the-art methods.
Naive Bayes Super-Resolution Forest
Authors: Jordi Salvador, Eduardo Perez-Pellitero
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Clustering, Super-Resolution

This paper presents a fast, high-performance method for super resolution with external learning. The first contribution leading to the excellent performance is a bimodal tree for clustering, which successfully exploits the antipodal invariance of the coarse-to-high-res mapping of natural image patches and provides scalability to finer partitions of the underlying coarse patch space. During training an ensemble of such bimodal trees is computed, providing different linearizations of the mapping. The second and main contribution is a fast inference algorithm, which selects the most suitable mapping function within the tree ensemble for each patch by adopting a Local Naive Bayes formulation. The experimental validation shows promising scalability properties that reflect the suitability of the proposed model, which may also be generalized to other tasks. The resulting method is beyond one order of magnitude faster and performs objectively and subjectively better than the current state of the art.
POP Image Fusion - Derivative Domain Image Fusion Without Reintegration
Authors: Graham D. Finlayson, Alex E. Hayes
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

There are many applications where multiple images are fused to form a single summary greyscale or colour output, including computational photography (e.g. RGB-NIR), diffusion tensor imaging (medical), and remote sensing. Often, and intuitively, image fusion is carried out in the derivative domain. Here, a new composite fused derivative is found that best accounts for the detail across all images and then the resulting gradient field is reintegrated. However, the reintegration step generally hallucinates new detail (not appearing in any of the input  image bands) including halo and bending artifacts. In this paper we avoid these hallucinated details by avoiding the reintegration step.  Our work builds directly on the work of Socolinsky and Wolff who derive their equivalent gradient field from the per-pixel Di Zenzo structure tensor which is defined as the inner product of the image Jacobian. We show that the x- and y- derivatives of the projection of the original image onto the Principal characteristic vector of the Outer Product (POP) of the Jacobian generates the same equivalent gradient field. In so doing, we have derived a fused image that has the derivative structure we seek. Of course, this projection will be meaningful only where the Jacobian has non-zero derivatives, so we diffuse the projection directions using a bilateral filter before we calculate the fused image. The resulting POP fused image has maximal fused detail but avoids hallucinated artifacts. Experiments demonstrate our method delivers state of the art image fusion performance.
Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising
Authors: Ying Fu, Antony Lam, Imari Sato, Yoichi Sato
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Denoising, Diagnostic, Dictionary Learning, Hyperspectral Image Denoising, Image Denoising

Hyperspectral imaging is beneficial in a diverse range of applications from diagnostic medicine, to agriculture, to surveillance to name a few. However, hyperspectral images often times suffer from degradation due to the limited light, which introduces noise into the imaging process. In this paper, we propose an effective model for hyperspectral image (HSI) denoising that considers underlying characteristics of HSIs: sparsity across the spatial-spectral domain, high correlation across spectra, and non-local self-similarity over space. We first exploit high correlation across spectra and non-local self-similarity over space in the noisy HSI to learn an adaptive spatial-spectral dictionary. Then, we employ the local and non-local sparsity of the HSI under the learned spatial-spectral dictionary to design an HSI denoising model, which can be effectively solved by an iterative numerical algorithm with parameters that are adaptively adjusted for different clusters and different noise levels. Experimental results on HSI denoising show that the proposed method can provide substantial improvements over the current state-of-the-art HSI denoising methods in terms of both objective metric and subjective visual quality.
Fully Connected Guided Image Filtering
Authors: Longquan Dai, Mengke Yuan, Feihu Zhang, Xiaopeng Zhang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12

This paper presents a linear time fully connected guided filter by introducing the minimum spanning tree (MST) to the guided filter (GF). Since the intensity based filtering kernel of GF is apt to overly smooth edges and the fixed-shape local box support region adopted by GF is not geometric-adaptive, our filter introduces an extra spatial term, the tree similarity, to the filtering kernel of GF and substitutes the box window with the implicit support region by establishing all-pairs-connections among pixels in the image and assigning the spatial-intensity-aware similarity to these connections. The adaptive implicit support region composed by the pixels with large kernel weights in the entire image domain has a big advantage over the predefined local box window in presenting the structure of an image for the reason that: 1, MST can efficiently present the structure of an image; 2, the kernel weight of our filter considers the tree distance defined on the MST. Due to these reasons, our filter achieves better edge-preserving results. We demonstrate the strength of the proposed filter in several applications. Experimental results show that our method produces better results than state-of-the-art methods.
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing
Authors: Feihu Zhang, Longquan Dai, Shiming Xiang, Xiaopeng Zhang
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: CPU, Optical Flow Estimation, Stereo Matching, Stereo Matching Hand

In this paper, we design a new edge-aware structure, named segment graph, to represent the image and we further develop a novel double weighted average image filter (SGF) based on the segment graph. In our SGF, we use the tree distance on the segment graph to define the internal weight function of the filtering kernel, which enables the filter to smooth out high-contrast details and textures while preserving major image structures very well. While for the external weight function, we introduce a user specified smoothing window to balance the smoothing effects from each node of the segment graph. Moreover, we also set a threshold to adjust the edge-preserving performance. These advantages make the SGF more flexible in various applications and overcome the "halo" and "leak" problems appearing in most of the state-of-the-art approaches. Finally and importantly, we develop a linear algorithm for the implementation of our SGF, which has an O(N) time complexity for both gray-scale and high dimensional images, regardless of the kernel size and the intensity range. Typically, as one of the fastest edge-preserving filters, our CPU implementation achieves 0.15s per megapixel when performing filtering for 3-channel color images. The strength of the proposed filter is demonstrated by various applications, including stereo matching, optical flow, joint depth map upsampling, edge-preserving smoothing, edges detection, image abstraction and texture editing.
Learning Ordinal Relationships for Mid-Level Vision
Authors: Daniel Zoran, Phillip Isola, Dilip Krishnan, William T. Freeman
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Depth Estimation, Intrinsic Image Decomposition

We propose a framework that infers mid-level visual properties of an image by learning about ordinal relation- ships. Instead of estimating metric quantities directly, the system proposes pairwise relationship estimates for points in the input image. These sparse probabilistic ordinal mea- surements are globalized to create a dense output map of continuous metric measurements. Estimating order rela- tionships between pairs of points has several advantages over metric estimation: it solves a simpler problem than metric regression; humans are better at relative judgements, so data collection is easier; ordinal relationships are invari- ant to monotonic transformations of the data, thereby in- creasing the robustness of the system and providing qualitatively different information. We demonstrate that this frame- work works well on two important mid-level vision tasks: intrinsic image decomposition and depth from an RGB im- age. We train two systems with the same architecture on data from these two modalities. We provide an analysis of the resulting models, showing that they learn a number of simple rules to make ordinal decisions. We apply our algo-rithm to depth estimation, with good results, and intrinsic image decomposition, with state-of-the-art results.
HARF: Hierarchy-Associated Rich Features for Salient Object Detection
Authors: Wenbin Zou, Nikos Komodakis
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Saliency Prediction, Salient Object Detection

The state-of-the-art salient object detection models are able to perform well for relatively simple scenes, yet for more complex ones, they still have difficulties in highlighting salient objects completely from background, largely due to the lack of sufficiently robust features for saliency prediction. To address such an issue, this paper proposes a novel hierarchy-associated feature construction framework for salient object detection, which is based on integrating elementary features from multi-level regions in a hierarchy. Furthermore, multi-layered deep learning features are introduced and incorporated as elementary features into this framework through a compact integration scheme. This leads to a rich feature representation, which is able to represent the context of the whole object/background and is much more discriminative as well as robust for salient object detection. Extensive experiments on the most widely used and challenging benchmark datasets demonstrate that the proposed approach substantially outperforms the state-of-the-art on salient object detection.
Image Matting With KL-Divergence Based Sparse Sampling
Authors: Levent Karacan, Aykut Erdem, Erkut Erdem
Date: 2015-12-01 00:00:00
Proceeding: ICCV 2015 12
Tasks: Image Matting

Previous sampling-based image matting methods typically rely on certain heuristics in collecting representative samples from known regions, and thus their performance deteriorates if the underlying assumptions are not satisfied. To alleviate this, in this paper we take an entirely new approach and formulate sampling as a sparse subset selection problem where we propose to pick a small set of candidate samples that best explains the unknown pixels. Moreover, we describe a new distance measure for comparing two samples which is based on KL-divergence between the distributions of features extracted in the vicinity of the samples. Using a standard benchmark dataset for image matting, we demonstrate that our approach provides more accurate results compared with the state-of-the-art methods.