nils-herrmann/clean_scirepeval huggingface dataset:
Ordinal Deep Feature Learning for Facial Age Estimation

In this paper, we propose an ordinal deep feature learning (ODFL) approach for facial age estimation. Unlike conventional age estimation methods which utilize hand-crafted features, our ODFL develops deep convolutional neural networks to learn discriminative feature descriptors directly from image pixels for face representation. Motivated by the fact that age labels are chronologically correlated and age estimation is an ordinal learning computer vision problem, we enforce two criterions on the descriptors which are learned at the top of our network: 1) the topology-aware ordinal relation of face samples is preserved in the learned feature space, and 2) the age difference information of the embedded feature representation is exploited in a ranking-preserving manner. Extensive experimental results on four face aging datasets show that our approach achieves promising performance compared with the state-of-the-art methods.
Nonlinear Structural Hashing for Scalable Video Search

In this paper, we propose a nonlinear structural hashing approach to learn compact binary codes for scalable video search. Unlike most existing video hashing methods which consider image frames within a video separately for binary code learning, we develop a multi-layer neural network to learn compact and discriminative binary codes by exploiting both the structural information between different frames within a video and the nonlinear relationship between video samples. To be specific, we learn these binary codes under two different constraints at the output of our network: 1) the distance between the learned binary codes for frames within the same scene is minimized and 2) the distance between the learned binary matrices for a video pair with the same label is less than a threshold and that for a video pair with different labels is larger than a threshold. To better measure the structural information of the scenes from videos, we employ a subspace clustering method to cluster frames into different scenes. Moreover, we design multiple hierarchical nonlinear transformations to preserve the nonlinear relationship between videos. Experimental results on three video data sets show that our method outperforms state-of-the-art hashing approaches on the scalable video search task.
Detection and segmentation of latent fingerprints

Latent fingerprints have been used by law enforcement agencies to identify suspects for a century. However, because of poor image quality and complex background noise, latent fingerprints are routinely identified relying on features manually marked by human experts in practice. A large number of latent fingerprints can not be treated in time due to lacking well trained experts, highlighting the need for “lights out” (fully-automatic) systems. In this paper, we propose a systematic algorithm for latent fingerprint detection, segmentation, and orientation field estimation, without any manual markup. Multiple potential latent fingerprints are detected using a sequential pose estimation algorithm. Then, the full orientation field and confidence map of each detected fingerprint are estimated based on localized dictionaries lookup. Finally, the boundary of each latent fingerprint is delineated by analyzing its confidence map. Experiments on a multi-latent fingerprint database and the challenging NIST SD27 latent fingerprint database show the effectiveness of the proposed algorithm.
Face Components Detection Using SURF Descriptors and SVMs

We present a feature-based method to classify salient points as belonging to objects in the face or background classes. We use SURF local descriptors (speeded up robust features) to generate feature vectors and use SVMs (support vector machines) as classifiers. Our system consists of a two-layer hierarchy of SVMs classifiers. On the first layer, a single classifier checks whether feature vectors are from face images or not. On the second layer, component labeling is operated using each component classifier of eye, mouth, and nose. This approach has the advantage about operating time because windows scanning procedure is not needed. Finally, this system performs the procedure to apply geometrical constraints to labeled descriptors. We show experimentally the efficiency of our approach.
GR2T vs L2E with Nuisance Scale

We compare the objective functions used by GR2T [1] and the L2E estimator [2] that have both been proposed for robust parameter estimation. We show their similarity when estimating location parameters. Of particular interest is their ability for dealing with the scale parameter that is often unknown and acts as a nuisance parameter. Both techniques are tested experimentally for regression (e.g. to find patterns such as line and circle in noisy datasets) and for registration between datasets.
Browsing sports video: trends in sports-related indexing and retrieval work

This paper aims to identify the current trends in sports-based indexing and retrieval work. It discusses the essential building blocks for any semantic-level retrieval system and acts as a case study in content analysis system design. While one of the major benefits of digital media and digital television in particular has been to provide users with more choices and a more interactive viewing experience, the freedom to choose has in fact manifested as the freedom to choose from the options the broadcaster provides. It is only through the use of automated content-based analysis that sports viewers will be given a chance to manipulate content at a much deeper level than that intended by broadcasters, and hence put true meaning into interactivity
Extension of GBVS to 3D media

Visual saliency has been studied extensively in the past decades through perceptual studies using eye tracking technologies and 2D displays. Visual saliency algorithms have been successfully developed to mimick the human ability to quickly spot informative local areas in images. This paper proposes to investigate the extension of visual saliency algorithms to media displayed in 3D. We show first that the Graph-Based Visual Saliency (GBVS) algorithm outperforms all the other common 2D algorithms as well as their 3D extensions. This paper then extends GBVS to 3D and shows that these new 3D GBVS based algorithms outperform other past algorithms.
3D shape estimation from silhouettes using mean-shift

In this article, a novel method to accurately estimate 3D surface of objects of interest is proposed. Each ray projected from 2D image plane to 3D space is modelled with the Gaussian kernel function. Then a mean shift algorithm with an annealing scheme is used to find maximums of the probability density function and recovers the 3D surface. Experimental results show that our method is more accurate to estimate 3D surface than the Radon transform-based approach.
Feature selection using visual saliency for content-based image retrieval

Saliency algorithms in content-based image retrieval are employed to retrieve the most important regions of an image with the idea that these regions hold the essence of representative information. Such regions are then typically analysed and described for future retrieval/classification tasks rather than the entire image itself - thus minimising computational resources required. We show that we can select a small number of features for indexing using a visual saliency measure without reducing the performance of classifiers trained to find objects.
Joint audio visual retrieval for tennis broadcasts

In recent years, there has been increasing work in the area of content retrieval for sports. The idea is generally to extract important events or create summaries to allow personalisation of the media stream. While previous work in sports analysis has employed either the audio or video stream to achieve some goal, there is little work that explores how much can be achieved by combining the two streams. This paper combines both audio and image features to identify the key episode in tennis broadcasts. The image feature is based on image moments and is able to capture the essence of scene geometry without recourse to 3D modelling. The audio feature uses PCA to identify the sound of the ball hitting the racket. The features are modelled as stochastic processes and the work combines the features using a likelihood approach. The results show that combining the features yields a much more robust system than using the features separately.
Classification and representation of semantic content in broadcast tennis videos

This paper investigates the semantic analysis of broadcast tennis footage. We consider the spatio-temporal behaviour of an object in the footage as being the embodiment of a semantic event. This object is tracked using a colour based particle filter. The video syntax and audio features are used to help delineate the temporal boundaries of these events. For broadcast tennis footage, the system firstly parses the video sequence based on the geometry of the content in view and classifies the clip as a particular view type. The temporal behaviour of the serving player is modelled using a HMM. As a result, each model is representative of a particular semantic episode. Events are then summarised using a number of synthesised keyframes.
Shape model fitting using non-isotropic GMM

We present a Mean Shift algorithm for fitting shape models. This algorithm maximises a posterior density function where the likelihood is defined as the Euclidean distance between two Gaussian mixture density functions, one modelling the observations while the other corresponds to the shape model. We explore the role of the covariance matrix in the Gaussian kernel for encoding the shape of the model in the density function. Results show that using non-isotropic covariance matrices improve the efficiency of the algorithm and allow to reduce the number of kernels to use in the mixture without compromising the robustness of the algorithm.
Modeling high level structure in sports with motion driven HMMs

In this paper, we investigate the retrieval of dynamic events that occur in broadcast sports footage. Dynamic events in sports are important in so far as they are related to the game semantics. Thus far, the temporal interleaving of camera views has been used to infer these types of events. We propose the use of the spatio-temporal behaviour of an object in the footage as an embodiment of a semantic event. This is accomplished by modeling the evolution of the position of the object with a hidden Markov model (HMM). Snooker is used as an example for the purpose of this research. The system firstly parses the video sequence based on the geometry of the content in the camera view and classifies the footage as a particular view type. Secondly, we consider the relative position of the white ball on the snooker table over the duration of a clip to embody semantic events. A colour based particle filter is employed to robustly track the snooker balls. The temporal behaviour of the white ball is modeled using a HMM where each model is representative of a particular semantic episode. Upon collision of the white ball with another coloured ball, a separate track is instantiated.
L2 registration for colour transfer

This paper proposes to perform colour transfer by minimising a divergence (the L2 distance) between two colour distributions. We propose to model each dataset by a compact Gaussian mixture which is designed for the specific purpose of colour transfer between images which have different scene content. A non rigid transformation is estimated by minimising the Euclidean distance (L2) between these two distributions and the estimated transformation is used for transfering colour statistics from one image to another. Experimental results show that this is a very promising approach for transferring colour and it performs very well against an alternative reference approach.
N-dimensional probability density function transfer and its application to color transfer

This article proposes an original method to estimate a continuous transformation that maps one N-dimensional distribution to another. The method is iterative, non-linear, and is shown to converge. Only 1D marginal distribution is used in the estimation process, hence involving low computation costs. As an illustration this mapping is applied to color transfer between two images of different contents. The paper also serves as a central focal point for collecting together the research activity in this area and relating it to the important problem of automated color grading
On summarising the ‘here and now’ of social videos for smart mobile browsing

The amount of media that is being uploaded to social sites (such as Twitter, Facebook and Instagram) is providing a wealth of visual data (images and videos) augmented with additional information such as keywords, timestamps and GPS coordinates. Tapastreet1 provides access in real-time to this visual content by harvesting social networks for visual media associated with particular locations, time and hashtags . Browsing efficiently through harvested videos requires smart processing to give users a quick overview of their content in particular when using mobile platforms with limited bandwidth. This paper aims at presenting an architecture for testing several strategies for processing summaries of videos collected on social networks to tackle this issue.
Statistical Hough Transform

The standard Hough transform is a popular method in image processing and is traditionally estimated using histograms. Densities modeled with histograms in high dimensional space and/or with few observations, can be very sparse and highly demanding in memory. In this paper, we propose first to extend the formulation to continuous kernel estimates. Second, when dependencies in between variables are well taken into account, the estimated density is also robust to noise and insensitive to the choice of the origin of the spatial coordinates. Finally, our new statistical framework is unsupervised (all needed parameters are automatically estimated) and flexible (priors can easily be attached to the observations). We show experimentally that our new modeling encodes better the alignment content of images.
Unsupervised statistical detection of changing objects in camera-in-motion video

Change detection in image sequences has mainly focused on the recovery of moving objects when the viewing system is static, or on the detection of simple production effects such as video shot boundaries or scene transitions. Camera motion is usually handled by the compensation of dominant motion, using motion estimation and segmentation schemes. We propose a novel statistical change detection method able to handle more complex events such as entering or exiting objects, or changes in object appearance, when the camera is moving. Temporal changes of objects are captured by analyzing the statistics of successive images. Considering an appropriate choice of image features, we show how it is possible to extract the statistics of changing objects from a pair of successive image histograms. Changing objects are then located by statistical backprojection techniques. The method is completely unsupervised and does not require any motion estimation or motion compensation. It is illustrated here on real world road scenes exhibiting large camera motion.
Estimating 3D Scene Flow from Multiple 2D Optical Flows

Scene flow is the motion of the surface points in the 3D world. For a camera, it is seen as a 2D optical flow in the image plane. Knowing the scene flow can be very useful as it gives an idea of the surface geometry of the objects in the scene and how those objects are moving. Four methods for calculating the scene flow given multiple optical flows have been explored and detailed in this paper along with the basic mathematics surrounding multi-view geometry. It was found that given multiple optical flows it is possible to estimate the scene flow to different levels of detail depending on the level of prior information present.
Bayesian classification for the Statistical Hough transform

We have introduced the statistical Hough transform that extends the standard Hough transform by using a kernel mixture as a robust alternative to the 2 dimensional accumulator histogram. This work develops further this framework by proposing a Bayesian classification scheme to associate the spatial coordinates (x, y) to one particular class defined in the Hough space (¿, ¿). In a first step, we segment the Hough space into meaningful classes. Then using the inverse Radon transform, we backproject the different classes into the image space. We illustrate our approach on a synthetic image and on real images.
Off-line multiple object tracking using candidate selection and the Viterbi algorithm

This paper presents a probabilistic framework for off-line multiple object tracking. At each timestep, a small set of deterministic candidates is generated which is guaranteed to contain the correct solution. Tracking an object within video then becomes possible using the Viterbi algorithm. In contrast with particle filter methods where candidates are numerous and random, the proposed algorithm involves a few candidates and results in a deterministic solution. Moreover, we consider here off-line applications where past and future information is exploited. This paper shows that, although basic and very simple, this candidate selection allows the solution of many tracking problems in different real-world applications and offers a good alternative to particle filter methods for off-line applications.
Smooth Kernel Density Estimate for Multiple View Reconstruction

We present a statistical framework to merge the information from silhouettes segmented in multiple view images to infer the 3D shape of an object. The approach is generalising the robust but discrete modelling of the visual hull by using the concept of averaged likelihoods. One resulting advantage of our framework is that the objective function is continuous and therefore an iterative gradient ascent algorithm can be defined to efficiently search the space. Moreover this results in a method which is less memory demanding and one that is very suitable to a parallel processing architecture. Experimental results shows that this approach is efficient for getting a robust initial guess to the 3D shape of an object in view
Multimodal Periodicity Analysis for Illicit Content Detection in Videos

In this paper, we propose a multimodal approach to illicit content detection in videos. Distribution of pornographic material over computer networks has been taking place since the inception of the internet. Until recently, most of the research focuses on illicit content detection in images and text. Typically it involves robust skin detection, texture characterisation, shape modelling and keyword filtering. Video however provides the opportunity of exploiting supplementary features including audio and motion for additional confidence in the classification process. This work investigates the use of visual motion information and periodicity in the audio stream for illicit content detection in videos.
3D Head Reconstruction Using Multi-camera Stream

Given information from many cameras, one can hope to get a complete 3D representation of an object. Pintavirooj and Sangworasil exploit this idea and present a system that records sequentially images from multiple view points to reconstruct a 3D shape of a static object of interest . For instance, using a 60 angle of view on the image, they manage to get its accurate 3D reconstruction . Unfortunately, when considering application such as video surveillance, it is not reasonable to expect that 60 cameras will give simultaneous images of a person of interest. However, we can expect that the person will move over time and show sequentially different poses of her/his head to at least one or a few cameras. This article proposes a technique for recovering an accurate 3D shape by combining views recorded at different times.
Impact of the Resolution on the Difference of Perceptual Video Quality Between CRT and LCD

The incoming of the high-definition new visual experience at home has boosted the new display technologies such as liquid crystal displays (LCD), plasma and projectors. These technologies enable the increase of the screen size necessary to sense a cinema-like experience. However, they introduce some new visual shortcomings not present with the mature CRT technology. In this paper, some subjective tests are described which highlight a difference of the perceptual video quality between CRT and LCD. Moreover, it's observed that this loss of quality on LCD is more important with high resolution sequences than with standard resolution ones. This influence of the resolution is particularly explainable in the case of the LCD motion blur defect.
Spatio-temporal segmentation of image sequences for object-oriented low bit-rate image coding

A method for coding of video sequences based on semantic decomposition into motion homogeneous regions is presented. The set of regions-the spatio-temporal segmentation-is intitialized for the first couple of frames of the sequence and then, tracked along the time axis, that allows to maintain its stability. Based on the spatio-temporal segmentation a predictive coding scheme is developed. Motion parameter vector and boundary description are encoded for each spatio temporal region. The bit-rate obtained for motion parameters is very low (<0.01 bits/pixel). The bit-rate for the boundary description strongly depends on the stability of the segmentation and varies around 0.1 bits/pixel. A costless transmission mode is choosen to encode the boundary component. The high visual quality of predicted frames with almost absolute absence of artifacts and a specific structure of prediction error allows the use of a selective coding of error signal.
Comparative study of some algorithms for terrain classification using SAR images

This paper describes the use of polarimetric SAR data for Earth terrain identification and classification. The K-distribution is used to describe the spatial characteristics of a forest area. Statistical methods using supervised and unsupervised techniques are also applied to segment the simple and the polarimetric data. The supervised classification is based on the ML algorithm assuming a Rayleigh distribution for the intensity pixels of the HH image and a circular Gaussian for the full polarimetric data. For the unsupervised classification, we use both the clustering and the ICM (iterative conditional modes) algorithms. Using such techniques, the segmentation we could classify areas of trees which differ by their age and which have in general an approachable scattering behaviour.<<ETX>>
Rectilinear structure extraction in textured images with an irregular, graph-based Markov random field model

This paper presents the application of Markov random field modelling to the extraction of poorly contrasted linear structures in textured areas. After a line feature detection step is performed, a set of straight line segments is derived from the feature image. This set of segments constitutes an irregular lattice which reproduces the image rectilinear pattern with accuracy. A Markov random field is then defined on this lattice, in order to group the sites that belong to the same structure. Finally the Markovian segmentation can be post-processed in order to extract global patterns. Results are given on pavement distress images.