nils-herrmann/clean_scirepeval huggingface dataset:
Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms
Kernel principle component analysis in pixels clustering

We propose two new methods in the nonlinear kernel feature space for pixel clustering based on the traditional KMeans and Gaussian mixture model (GMM). Unlike the previous work on the kernel machines, we give out a new perspective on the new developed kernel machines. That is, kernel principle component analysis (KPCA) combined with the KMeans and the GMM are kernel KMeans (KKMeans) and kernel GMM (KGMM), respectively. In this paper, we prove the new perspective on KKMeans and give out a clear statement on the KGMM as well. Based on this new perspectives, we can implement the KKMeans and the KGMM conveniently. At the end of the paper, we utilize these new algorithms on the problem of the colour image segmentation. Based on a series of experimental results on Corel colour images, we find that the KKMeans and KGMM can outperform the traditional KMeans and GMM consistently, respectively.
DISC: Deep Image Saliency Computing via Progressive Representation Learning

Salient object detection increasingly receives attention as an important component or step in several pattern recognition and image processing tasks. Although a variety of powerful saliency models have been intensively proposed, they usually involve heavy feature (or model) engineering based on priors (or assumptions) about the properties of objects and backgrounds. Inspired by the effectiveness of recently developed feature learning, we provide a novel deep image saliency computing (DISC) framework for fine-grained image saliency computing. In particular, we model the image saliency from both the coarse-and fine-level observations, and utilize the deep convolutional neural network (CNN) to learn the saliency representation in a progressive manner. In particular, our saliency model is built upon two stacked CNNs. The first CNN generates a coarse-level saliency map by taking the overall image as the input, roughly identifying saliency regions in the global context. Furthermore, we integrate superpixel-based local context information in the first CNN to refine the coarse-level saliency map. Guided by the coarse saliency map, the second CNN focuses on the local context to produce fine-grained and accurate saliency map while preserving object details. For a testing image, the two CNNs collaboratively conduct the saliency computing in one shot. Our DISC framework is capable of uniformly highlighting the objects of interest from complex background while preserving well object details. Extensive experiments on several standard benchmarks suggest that DISC outperforms other state-of-the-art methods and it also generalizes well across data sets without additional training. The executable version of DISC is available online: <;uri xlink:type="simple">http://vision.sysu.edu.cn/projects/DISC<;/uri>.
Projected clustering via robust orthogonal least square regression with optimal scaling

The orthogonal least square regression (OLSR) serves as a pretty significant problem for the dimensionality reduction. Due to lack of the scale change in OLSR, the scaling term is at first introduced to OLSR to build up a novel orthogonal least square regression with optimal scaling (OLSR-OS) problem. However, OLSR-OS is still sensitive to the outliers, such that associated results could be fallacious. To strengthen the robustness of OLSR-OS, we propose an original robust OLSR-OS (ROLSR-OS) problem in ℓ2,1-norm. To tackle a more ill-defined situation, ROLSR-OS in ℓ2,1-norm can be further extended to ROLSR-OS in capped ℓ2-norm. Besides, the associated ROLSR-OS methods could be derived by solving the re-weighted counterparts of ROLSR-OS problems in both norms. Moreover, the equivalence between the re-weighted counterparts and the original ROLSR-OS problems is also provided along with the convergence analysis of the proposed ROLSR-OS methods. Accordingly, both the optimal scaling and weight can be achieved automatically via the proposed ROLSR-OS approaches. Specifically, the proposed ROLSR-OS methods are self-adaptive, such that the smaller weight would be automatically assigned to the term with larger outliers to enhance the robustness. Consequently, projected clustering and modified projected clustering under the proposed ROLSR-OS problems are further investigated both theoretically and experimentally.
Relevance Preserving Projection and Ranking for Web Image Search Reranking

An image search reranking (ISR) technique aims at refining text-based search results by mining images' visual content. Feature extraction and ranking function design are two key steps in ISR. Inspired by the idea of hypersphere in one-class classification, this paper proposes a feature extraction algorithm named hypersphere-based relevance preserving projection (HRPP) and a ranking function called hypersphere-based rank (H-Rank). Specifically, an HRPP is a spectral embedding algorithm to transform an original high-dimensional feature space into an intrinsically low-dimensional hypersphere space by preserving the manifold structure and a relevance relationship among the images. An H-Rank is a simple but effective ranking algorithm to sort the images by their distances to the hypersphere center. Moreover, to capture the user's intent with minimum human interaction, a reversed k-nearest neighbor (KNN) algorithm is proposed, which harvests enough pseudorelevant images by requiring that the user gives only one click on the initially searched images. The HRPP method with reversed KNN is named one-click-based HRPP (OC-HRPP). Finally, an OC-HRPP algorithm and the H-Rank algorithm form a new ISR method, H-reranking. Extensive experimental results on three large real-world data sets show that the proposed algorithms are effective. Moreover, the fact that only one relevant image is required to be labeled makes it has a strong practical significance.
A novel image quality metric based on morphological component analysis

Due to that human eye has different perceptual characteristics for different morphological components, so a novel image quality metric is proposed by incorporating morphological component analysis (MCA) and human visual system (HVS), which is capable of assessing the image with different types of distortion. Firstly, reference and distorted images are decomposed into texture and cartoon components by MCA respectively. Then these components are changed into perceptual features by just noticeable difference (JND) which integrates masking features, luminance adaptation and contrast sensitive function (CSF). Finally, the difference between reference and distorted images' perceptual features is quantified using a pooling strategy, and then the final result of the image quality is obtained. Experimental results demonstrate that the performance of the metric prevail over some existing methods on LIVE database II.
Robust Sparse Coding for Mobile Image Labeling on the Cloud

With the rapid development of the mobile service and online social networking service, a large number of mobile images are generated and shared on the social networks every day. The visual content of these images contains rich knowledge for many uses, such as social categorization and recommendation. Mobile image labeling has, therefore, been proposed to understand the visual content and received intensive attention in recent years. In this paper, we present a novel mobile image labeling scheme on the cloud, in which mobile images are first and efficiently transmitted to the cloud by Hamming compressed sensing, such that the heavy computation for image understanding is transferred to the cloud for quick response to the queries of the users. On the cloud, we design a sparse correntropy framework for robustly learning the semantic content of mobile images, based on which the relevant tags are assigned to the query images. The proposed framework (called maximum correntropy-based mobile image labeling) is very insensitive to the noise and the outliers, and is optimized by a half-quadratic optimization technique. We theoretically show that our image labeling approach is more robust than the squared loss, absolute loss, Cauchy loss, and many other robust loss function-based sparse coding methods. To further understand the proposed algorithm, we also derive its robustness and generalization error bounds. Finally, we conduct experiments on the PASCAL VOC'07 data set and empirically demonstrate the effectiveness of the proposed robust sparse coding method for mobile image labeling.
Embedded clustering via robust orthogonal least square discriminant analysis

In this paper, a novel embedded clustering (EC) method is derived from the perspective of extending the supervised orthogonal least square discriminant analysis (OLSDA) method to the unsupervised case, which proves to be closely related to k-means. To achieve more statistical and structural properties, the robust learning of unsupervised OLSDA is investigated to further derive the unsupervised robust OLSDA (ROLSDA) problem. For the convenience of solving the proposed ROLSDA problem, re-weighted counterpart of ROLSDA is utilized with self-adaptive weight, such that the smaller weight would be assigned to the term with larger outliers automatically. Consequently, aforementioned EC method is proposed with not only the robust outliers but also the optimal weighted cluster centroids. Comparative experiments are presented to show the effectiveness of the EC method under the proposed ROLSDA problem.
Discrete Nonnegative Spectral Clustering

Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones), which may cause unpredictable deviation of resultant cluster labels from genuine ones, thereby leading to severe information loss and performance degradation. In this work, we study how to achieve discrete clustering as well as reliably generalize to unseen data. We propose a novel spectral clustering scheme which deeply explores cluster label properties, including discreteness, nonnegativity, and discrimination, as well as learns robust out-of-sample prediction functions. Specifically, we explicitly enforce a discrete transformation on the intermediate continuous labels, which leads to a tractable optimization problem with a discrete solution. Besides, we preserve the natural nonnegative characteristic of the clustering labels to enhance the interpretability of the results. Moreover, to further compensate the unreliability of the learned clustering labels, we integrate an adaptive robust module with $\ell _{2,p}$  loss to learn prediction function for grouping unseen data. We also show that the out-of-sample component can inject discriminative knowledge into the learning of cluster labels under certain conditions. Extensive experiments conducted on various data sets have demonstrated the superiority of our proposal as compared to several existing clustering approaches.
Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e.g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.
Image Classification With Densely Sampled Image Windows and Generalized Adaptive Multiple Kernel Learning

We present a framework for image classification that extends beyond the window sampling of fixed spatial pyramids and is supported by a new learning algorithm. Based on the observation that fixed spatial pyramids sample a rather limited subset of the possible image windows, we propose a method that accounts for a comprehensive set of windows densely sampled over location, size, and aspect ratio. A concise high-level image feature is derived to effectively deal with this large set of windows, and this higher level of abstraction offers both efficient handling of the dense samples and reduced sensitivity to misalignment. In addition to dense window sampling, we introduce generalized adaptive ℓp-norm multiple kernel learning (GA-MKL) to learn a robust classifier based on multiple base kernels constructed from the new image features and multiple sets of prelearned classifiers from other classes. With GA-MKL, multiple levels of image features are effectively fused, and information is shared among different classifiers. Extensive evaluation on benchmark datasets for object recognition (Caltech256 and Caltech101) and scene recognition (15Scenes) demonstrate that the proposed method outperforms the state-of-the-art under a broad range of settings.
Weakly Supervised Multilabel Clustering and its Applications in Computer Vision.

Clustering is a useful statistical tool in computer vision and machine learning. It is generally accepted that introducing supervised information brings remarkable performance improvement to clustering. However, assigning accurate labels is expensive when the amount of training data is huge. Existing supervised clustering methods handle this problem by transferring the bag-level labels into the instance-level descriptors. However, the assumption that each bag has a single label limits the application scope seriously. In this paper, we propose weakly supervised multilabel clustering, which allows assigning multiple labels to a bag. Based on this, the instance-level descriptors can be clustered with the guidance of bag-level labels. The key technique is a weakly supervised random forest that infers the model parameters. Thereby, a deterministic annealing strategy is developed to optimize the nonconvex objective function. The proposed algorithm is efficient in both the training and the testing stages. We apply it to three popular computer vision tasks: 1) image clustering; 2) semantic image segmentation; and 3) multiple objects localization. Impressive performance on the state-of-the-art image data sets is achieved in our experiments.
Heterogeneous Face Recognition: A Common Encoding Feature Discriminant Approach

Heterogeneous face recognition is an important, yet challenging problem in face recognition community. It refers to matching a probe face image to a gallery of face images taken from alternate imaging modality. The major challenge of heterogeneous face recognition lies in the great discrepancies between different image modalities. Conventional face feature descriptors, e.g., local binary patterns, histogram of oriented gradients, and scale-invariant feature transform, are mostly designed in a handcrafted way and thus generally fail to extract the common discriminant information from the heterogeneous face images. In this paper, we propose a new feature descriptor called common encoding model for heterogeneous face recognition, which is able to capture common discriminant information, such that the large modality gap can be significantly reduced at the feature extraction stage. Specifically, we turn a face image into an encoded one with the encoding model learned from the training data, where the difference of the encoded heterogeneous face images of the same person can be minimized. Based on the encoded face images, we further develop a discriminant matching method to infer the hidden identity information of the cross-modality face images for enhanced recognition performance. The effectiveness of the proposed approach is demonstrated (on several public-domain face datasets) in two typical heterogeneous face recognition scenarios: matching NIR faces to VIS faces and matching sketches to photographs.
A novel segmentation guided approach for single image dehazing

This paper presents a novel image haze removal approach from single image. In the algorithm, the constant albedo and dark channel prior methods are combined to represent the transmission model of hazed image. And then, the watershed segmentation approach is introduced to decompose the input image into some gray level consistent areas. Compared with traditional fixed image partition schemes, better estimation of the atmospheric light can be obtained as well as to avoid the problem of halo artifacts. With the improved haze image modeling approach and atmospheric light estimation, the dehazed image with better visual quality can be achieved.
Geometrically invariant watermarking using affine covariant regions

In this paper, we present a robust approach to digital watermarking embedding and retrieval for digital images. The affine-invariant point detector is used to extract feature regions of the given host image. Image normalization and dominant gradient orientation alignment are applied to these feature regions to achieve scaling and rotation invariance. Thereafter, watermarks are embedded in these feature regions. The advantage of this proposed approach is demonstrated by a large amount of experiments, which can against different attacks, such as common image processing, rotation, scaling, cropping, and several other affine transformations.
A General Framework for Edited Video and Raw Video Summarization

In this paper, we build a general summarization framework for both of edited video and raw video summarization. Overall, our work can be divided into three folds. 1) Four models are designed to capture the properties of video summaries, i.e., containing important people and objects (importance), representative to the video content (representativeness), no similar key-shots (diversity), and smoothness of the storyline (storyness). Specifically, these models are applicable to both edited videos and raw videos. 2) A comprehensive score function is built with the weighted combination of the aforementioned four models. Note that the weights of the four models in the score function, denoted as property-weight, are learned in a supervised manner. Besides, the property-weights are learned for edited videos and raw videos, respectively. 3) The training set is constructed with both edited videos and raw videos in order to make up the lack of training data. Particularly, each training video is equipped with a pair of mixing-coefficients, which can reduce the structure mess in the training set caused by the rough mixture. We test our framework on three data sets, including edited videos, short raw videos, and long raw videos. Experimental results have verified the effectiveness of the proposed framework.
Person Re-Identification by Regularized Smoothing KISS Metric Learning

With the rapid development of the intelligent video surveillance (IVS), person re-identification, which is a difficult yet unavoidable problem in video surveillance, has received increasing attention in recent years. That is because computer capacity has shown remarkable progress and the task of person re-identification plays a critical role in video surveillance systems. In short, person re-identification aims to find an individual again that has been observed over different cameras. It has been reported that KISS metric learning has obtained the state of the art performance for person re-identification on the VIPeR dataset . However, given a small size training set, the estimation to the inverse of a covariance matrix is not stable and thus the resulting performance can be poor. In this paper, we present regularized smoothing KISS metric learning (RS-KISS) by seamlessly integrating smoothing and regularization techniques for robustly estimating covariance matrices. RS-KISS is superior to KISS, because RS-KISS can enlarge the underestimated small eigenvalues and can reduce the overestimated large eigenvalues of the estimated covariance matrix in an effective way. By providing additional data, we can obtain a more robust model by RS-KISS. However, retraining RS-KISS on all the available examples in a straightforward way is time consuming, so we introduce incremental learning to RS-KISS. We thoroughly conduct experiments on the VIPeR dataset and verify that 1) RS-KISS completely beats all available results for person re-identification and 2) incremental RS-KISS performs as well as RS-KISS but reduces the computational cost significantly.
A Survey of Sparse Representation: Algorithms and Applications

Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition. Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. The main purpose of this paper is to provide a comprehensive study and an updated review on sparse representation and to supply guidance for researchers. The taxonomy of sparse representation methods can be studied from various viewpoints. For example, in terms of different norm minimizations used in sparsity constraints, the methods can be roughly categorized into five groups: 1) sparse representation with l0-norm minimization; 2) sparse representation with lp-norm (0 <; p <; 1) minimization; 3) sparse representation with l1-norm minimization; 4) sparse representation with l2,1-norm minimization; and 5) sparse representation with l2-norm minimization. In this paper, a comprehensive overview of sparse representation is provided. The available sparse representation algorithms can also be empirically categorized into four groups: 1) greedy strategy approximation; 2) constrained optimization; 3) proximity algorithm-based optimization; and 4) homotopy algorithm-based sparse representation. The rationales of different algorithms in each category are analyzed and a wide range of sparse representation applications are summarized, which could sufficiently reveal the potential nature of the sparse representation theory. In particular, an experimentally comparative study of these sparse representation algorithms was presented.