nils-herrmann/clean_scirepeval huggingface dataset:
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.
Spectral–Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning

In this paper, we propose a new framework for spectral-spatial classification of hyperspectral image data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously. An important contribution of our paper is the fact that we exploit the marginal probability distribution which uses the whole information in the hyperspectral data. We learn such distributions from both the spectral and spatial information contained in the original hyperspectral data using loopy belief propagation. The adopted probabilistic model is a discriminative random field in which the association potential is a multinomial logistic regression classifier and the interaction potential is a Markov random field multilevel logistic prior. Our experimental results with hyperspectral data sets collected using the National Aeronautics and Space Administration's Airborne Visible Infrared Imaging Spectrometer and the Reflective Optics System Imaging Spectrometer system indicate that the proposed framework provides state-of-the-art performance when compared to other similar developments.
Semi-supervised active learning for urban hyperspectral image classification

In this paper, we develop a new framework for semi-supervised learning which exploits active learning for unlabeled sample selection in hyperspectral data classification. Specifically, we use active learning to select the most informative unlabeled training samples with the ultimate goal of systematically achieving noticeable improvements in classification results with regard to those found by randomly selected training sets of the same size. Our experimental results, conducted with an urban hyperspectral scene collected by the Reflective Optics Spectrographic Imaging Instrument (ROSIS) of the Deutschen Zentrum for Luftund Raumfahrt (DLR, the German Aerospace Agency) over the city of Pavia, Italy, indicate that using active learning for unlabeled sample selection represents an effective and promising strategy in the context of urban hyperspectral data classification.
Bayesian Hyperspectral Image Segmentation With Discriminative Class Learning

This paper introduces a new supervised technique to segment hyperspectral images: the Bayesian segmentation based on discriminative classification and on multilevel logistic (MLL) spatial prior. The approach is Bayesian and exploits both spectral and spatial information. Given a spectral vector, the posterior class probability distribution is modeled using multinomial logistic regression (MLR) which, being a discriminative model, allows to learn directly the boundaries between the decision regions and, thus, to successfully deal with high-dimensionality data. To control the machine complexity and, thus, its generalization capacity, the prior on the multinomial logistic vector is assumed to follow a componentwise independent Laplacian density. The vector of weights is computed via the fast sparse multinomial logistic regression (FSMLR), a variation of the sparse multinomial logistic regression (SMLR), conceived to deal with large data sets beyond the reach of the SMLR. To avoid the high computational complexity involved in estimating the Laplacian regularization parameter, we have also considered the Jeffreys prior, as it does not depend on any hyperparameter. The prior probability distribution on the class-label image is an MLL Markov-Gibbs distribution, which promotes segmentation results with equal neighboring class labels. The -expansion optimization algorithm, a powerful graph-cut-based integer optimization tool, is used to compute the maximum a posteriori segmentation. The effectiveness of the proposed methodology is illustrated by comparing its performance with the state-of-the-art methods on synthetic and real hyperspectral image data sets. The reported results give clear evidence of the relevance of using both spatial and spectral information in hyperspectral image segmentation.
Object proposals using SVM-based integrated model

Utilizing object proposals as a preprocessing procedure has been shown its significance in many multimedia computing tasks. Most state-of-the-art methods devoted to finding a generic objectness measure for rating the possibilities of the initial sliding windows with or without objects. In fact, the object criteria vary from one objectness measure to another, which leads to the definite bottleneck for the single method. By observing the performance of the state-of-the-art in the large dataset, an integrated objectness model is proposed in this paper by accumulating the advantages from selected state-of-the-art techniques. First, the initial bounding boxes are generated by the strategy as same as the method with the highest object detection rate and slowest intersection over union drop. Second, these candidate boxes are re-scored based on each method's objectness system. Then, a score feature is obtained for each bounding box. A support vector machines (SVM) is utilized to train a general model on the training set constructed from a series of score vectors and the probabilistic scores for the testing boxes are predicted according to the learned model. The final proposals are ranked on account of the predicted scores. The evaluation on the challenging PASCAL VOC 2007 dataset shows that the proposed method has dominant concentration with better performance compared to the single state-of-the-art method.
Saliency cuts based on adaptive triple thresholding

Salient object detection attracts much attention for its effectiveness in numerous applications. However, how to effectively produce a high quality binary mask from a saliency map, named saliency cuts, is still an open problem. In this paper, we propose a novel saliency cuts approach using unsupervised seeds generation and GrabCut algorithm. With the input of a saliency map, we produce seeds for segmentation using adaptive triple thresholding, and feed the seeds to GrabCut algorithm. Finally, a high quality object mask is generated by iteratively optimization. The experimental results show that the proposed approach is competent to the task of saliency cuts and outperforms the state-of-the-art methods.
Object trajectory proposal

We propose a novel method for video object proposal to generate sequences of bounding boxes for each object candidate in videos, namely object trajectory proposals. Unlike the image-based methods that produce object proposals independently in each video frame, our method generates temporally consistent proposals in the form of object trajectories, which is crucial for subsequent analysis of object appearance and motion characteristics. Given a video, we extract motion seeds through estimating the outliers of global motion, and generate moving object trajectory proposals from the seeds. By ignoring the motion outliers, we consistently sample bounding boxes with pruning to form static object trajectory proposals. Finally, we rank both the moving and static object trajectory proposals under a unified scoring mechanism. The experimental results show that our method can effectively generate object trajectory proposals and outperform the state-of-the-art methods.
Depth-Aware Salient Object Detection and Segmentation via Multiscale Discriminative Saliency Fusion and Bootstrap Learning

This paper proposes a novel depth-aware salient object detection and segmentation framework via multiscale discriminative saliency fusion (MDSF) and bootstrap learning for RGBD images (RGB color images with corresponding Depth maps) and stereoscopic images. By exploiting low-level feature contrasts, mid-level feature weighted factors and high-level location priors, various saliency measures on four classes of features are calculated based on multiscale region segmentation. A random forest regressor is learned to perform the discriminative saliency fusion (DSF) and generate the DSF saliency map at each scale, and DSF saliency maps across multiple scales are combined to produce the MDSF saliency map. Furthermore, we propose an effective bootstrap learning-based salient object segmentation method, which is bootstrapped with samples based on the MDSF saliency map and learns multiple kernel support vector machines. Experimental results on two large datasets show how various categories of features contribute to the saliency detection performance and demonstrate that the proposed framework achieves the better performance on both saliency detection and salient object segmentation.
StereoSnakes: Contour Based Consistent Object Extraction for Stereo Images

Consistent object extraction plays an essential role for stereo image editing with the population of stereoscopic 3D media. Most previous methods perform segmentation on entire images for both views using dense stereo correspondence constraints. We find that for such kind of methods the computation is highly redundant since the two views are near-duplicate. Besides, the consistency may be violated due to the imperfectness of current stereo matching algorithms. In this paper, we propose a contour based method which searches for consistent object contours instead of regions. It integrates both stereo correspondence and object boundary constraints into an energy minimization framework. The proposed method has several advantages compared to previous works. First, the searching space is restricted in object boundaries thus the efficiency significantly improved. Second, the discriminative power of object contours results in a more consistent segmentation. Furthermore, the proposed method can effortlessly extend existing single-image segmentation methods to work in stereo scenarios. The experiment on the Adobe benchmark shows superior extraction accuracy and significant improvement of efficiency of our method to state-of-the-art. We also demonstrate in a few applications how our method can be used as a basic tool for stereo image editing.
User-oriented stereo video refocusing by computational cinematographic model

Refocusing, as a most popular photographic technique, is widely welcomed in both photography and cinematography. Currently, most refocusing effects in movies are implemented manually using professional devices or computer graphics methods. Few solutions are designed for the videos captured by common users. In this paper, we propose a user-oriented method to facilitate the video refocusing for daily life, with an extra requirement for only stereo cameras which are quite popular today. Given a stereo video, one can select the desired focusing part in a single frame, and the user selected intention will be tracked along with the timeline. Then the depth-of-field (DoF) based blurring effect can be generated by scene depth estimation based on the stereo techniques and the proposed cinematographic models in computational photography. The performance of the refocused videos compares favourably with the ones generated by digital single lens reflex (DSLR). To evaluate the proposed method, we conduct the experiments on several challenging stereo video datasets. The quantitative and subjective evaluations both show that the proposed method can achieve attractive and highly aesthetic performance with few user interactions.
Depth saliency based on anisotropic center-surround difference

Most previous works on saliency detection are dedicated to 2D images. Recently it has been shown that 3D visual information supplies a powerful cue for saliency analysis. In this paper, we propose a novel saliency method that works on depth images based on anisotropic center-surround difference. Instead of depending on absolute depth, we measure the saliency of a point by how much it outstands from surroundings, which takes the global depth structure into consideration. Besides, two common priors based on depth and location are used for refinement. The proposed method works within a complexity of O(N) and the evaluation on a dataset of over 1000 stereo images shows that our method outperforms state-of-the-art.
A Motion-Insensitive Dissolve Detection Method with SURF

As dissolve is the most common gradual shot transition, dissolve detection plays an important role in video segmentation which is the fundamental step for efficient video indexing and retrieval. However, the existing detection methods easily confuse dissolve with camera motion or object motion when using global features. Besides, when using local features’ change tendency, they can’ t get accurate trajectories to reflect this. In this paper, we propose a SURF feature based dissolve detection algorithm which can well differentiate dissolve from motion appearances. We get the trajectories of SURF key points by matching between two successive frames. Then a candidate set of dissolves is obtained according to the distribution of the starting points and ending points on the trajectory, filtering part of motions. Dissolves are further located by analyzing the curve of the proportion of subtrajectories with monotonous variation through each frame. Experiments demonstrate the effectiveness and efficiency of the proposed method.
Constrained sampling for image retargeting

In this paper, we present a new approach for retargeting large images to mobile devices with small screens. As the core of image retargeting, information fidelity is adequately considered in terms of reservations of salient regions, edge integrity, and image layout. By taking these aspects as constraints, image retargeting is formulated as a constrained sampling task. Each pixel in image is first represented with a vector encoding the constraints. Then, pixels with the same vector values combine to form blocks, and the original image is thus converted into a graph representation. Thereafter, the sampling ratio of each block is determined with a balanced minimum cost flow algorithm. Final result is generated by an interpolated sampling scheme and direct scaling. Experiments demonstrate the effectiveness of the proposed approach.
Full-Reference Quality Assessment for Video Summary

As video summarization techniques have attracted more and more attention for efficient multimedia data management, quality assessment of video summary is required. To address the lack of automatic evaluation techniques, this paper proposes a novel framework including several new algorithms to assess the quality of the video summary against a given reference. First, we partition the reference video summary and the candidate video summary into the sequences of summary unit (SU). Then, we utilize alignment based algorithm to match the SUs in the candidate summary with the SUs in the corresponding reference summary. Third, we propose a novel similarity based 4 C - assessment algorithm to evaluate the candidate video summary from the perspective of coverage, conciseness, coherence, and context, respectively. Finally, the individual assessment results are integrated according to userpsilas requirement by a learning based weight adaptation method. The proposed framework and techniques are experimented on a standard dataset of TRECVID 2007 and show the good performance in automatic video summary assessment.
Video salient object detection via cross-frame cellular automata

Salient object detection aims to detect the attractive objects on images and videos. In this paper, we propose a novel salient object detection method for videos based on cross-frame cellular automata. Given a video, we first represent the video frames with super-pixels, and construct a saliency propagation network among super-pixels within a frame and between adjacent frames based on their appearance similarities and temporal coherency. Second, we initialize the saliency map of each frame with the fusion of two saliency maps generated by appearance and motion features independently. Finally, we utilize cellular automata updating to propagate saliency among super-pixels iteratively and generate the coherent saliency maps with complete objects. The experimental results show that our method outperforms the state-of-the-art methods on different types of videos.
Rapid image retargeting based on curve-edge grid representation

Image retargeting technique attracts more and more attention for convenient image display on mobile devices. However, current methods can't well balance the retargeting efficiency and effectiveness, which limits their applications on the mobile devices with low computing ability. In this paper, we propose a novel image retargeting approach by combining uniform sampling and structure-aware curve-edge grid representation. We first decompose the original image into curve-edge grids by dynamic programming, and then generate the target image by uniformly sampling the pixels within the grids. The simplicity of retargeting procedure and sampling strategy enables our approach to easily achieve good computational efficiency. Furthermore, the constraint of curve-edge grid representation ensures important content emphasis and image structure preservation in the target image. Experiments on different images demonstrate the effectiveness and efficiency of our approach.
Multi-operator Image Retargeting Based on Automatic Quality Assessment

Image retargeting aims to avoid visual distortion while retaining important image content in resizing. However, no single image retargeting method can handle all cases. In this paper, we propose a novel multi-operator image retargeting approach, which utilizes an efficient and human perception based automatic quality assessment in operator selection. First, we calculate the importance map and distortion map for quality assessment. Then, we construct the resizing space and assess the performance of each operator in iterative width and/or height reduction. Finally, we select the optimal operator sequence by dynamic programming and generate the target image. Experiments demonstrate the effectiveness of the proposed approach.
Image retargeting based on global energy optimization

This paper proposes a novel image retargeting technique based on global energy optimization. Most existing methods enhance the high energy parts of the original image by pre-defined strategies or local optimization based iterations. They can not achieve the global optimal effect in energy retainment. To solve this problem, our approach formulates image retargeting as a global optimization problem on energy. We first calculate the energy map of the original image. Then, we utilize a constrained linear programming to maximize the retained energy in retargeting. Finally, we propose a pixel fusion based method to generate the retargeted image. To make it more feasible in implementation, we further provide two strategies to reduce the time cost of our approach. We demonstrate the proposed approach by comparing with typical image retargeting methods.
Automatic image retargeting evaluation based on user perception

As image retargeting techniques have attracted more and more attention for effective image display on mobile devices, quality evaluation of image retargeting is required. To address the lack of automatic evaluation techniques in retargeting, this paper proposes a user perception based framework to automatically evaluate the quality of the target image against the original image. In the framework, the pixels in the original image and the target image are first approximately order-preserved matched by dynamic programming. Based on the pixel matching result, several features are extracted to describe the user requirements and further adapted to fit user perception in retargeting. Finally, the overall score of the target image quality is calculated by integrating the scores in different evaluation aspects. Experiments demonstrate the effectiveness of the proposed framework.
Depth-aware layered edge for object proposal

Object proposal, typically served as preprocessing of various multimedia applications, aims to detect the bounding boxes of possible objects in an image. In this paper, we propose a novel object proposal method for RGB-D images based on layered edges, which can effectively eliminate the influence of the mixture of edges from objects and background and improve the accuracy of proposals. Firstly, we detect the sparse edges and correct depth on super-pixel representation. Then, we use depth-adaptive sliding windows in sampling of depth distribution and measure the objectness of each candidate box in multiple depth layers. Finally, the candidate boxes are ranked according to the integrated scores of all the depth layers, and the final proposals are generated. The experimental results show that the proposed method can outperform the state-of-the-art methods on the largest RGB-D image dataset for object proposal.
Salient object detection for RGB-D image via saliency evolution

Salient object detection aims to detect the most attractive objects in images, which has been widely used as a fundamental of various multimedia applications. In this paper, we propose a novel salient object detection method for RGB-D images based on evolution strategy. Firstly, we independently generate two saliency maps on color channel and depth channel of a given RGB-D image based on its super-pixels representation. Then, we fuse the two saliency maps with refinement to provide an initial saliency map with high precision. Finally, we utilize cellular automata to iteratively propagate saliency on the initial saliency map and generate the final detection result with complete salient objects. The proposed method is evaluated on two public RGB-D datasets, and the experimental results show that our method outperforms the state-of-the-art methods.