nils-herrmann/clean_scirepeval huggingface dataset:
Shot boundary detection for video temporal segmentation based on the weber local descriptor

Despite its associated challenges, the development of mechanisms for storing, indexing, transmitting and visualizing multimedia content is crucial in order to efficiently deal with the large amount of data generated by several different sources. Digital video technology has advanced rapidly, such that temporal video segmentation methods for automatically detecting transitions in video sequences play an important role in the content analysis tasks. In this work, we propose and evaluate a video shot boundary detection approach based on the Weber local descriptor. Experiments conducted on different datasets demonstrate the effectiveness of our method, whose results are compared against other approaches of the literature.
Dimensionality reduction through PCA over SIFT and SURF descriptors

One of the constant challenges in image analysis is to improve the process for obtaining distinctive object characteristics. Feature descriptors usually demand high dimensionality to adequately represent the objects of interest. The higher the dimensionality, the greater the consumption of resources such as memory space and computational time. Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) present algorithms that, besides of detecting interest points accurately, extract well suited feature descriptors. The problem with these feature descriptors is their high dimensionality. There have been several works attempting to confront the curse of dimensionality over some of the developed descriptors. In this paper, we apply Principal Component Analysis (PCA) to reduce SIFT and SURF feature vectors in order to perform the task of having an accurate low-dimensional feature vector. We evaluate such low-dimensional feature vectors in a matching application, as well as their distinctiveness in image retrieval. Finally, the required resources in computational time and memory space to process the original descriptors are compared to those resources consumed by the new low-dimensional descriptors.
Boolean decomposition of binary image operators

Boolean expansion of binary functions has been used in the representation of binary image operators. This representation scheme induced the creation of image processing architectures based on decision diagrams. In some situations, the application of this graph-based operator over the input images is faster than the corresponding conventional approach, in that the operators are constructively described by means of basic operators and set operations. However, when the conventional architecture exploits the bit parallelism of the microprocessor logical instructions, its performance often surpasses that of the graph-based system. This article proposes a novel approach, in which the decision diagrams guide the actions of the underlying conventional architecture, taking advantage of its parallelism, so that faster image processing can be achieved.
Adaptive video shot detection improved by fusion of dissimilarity measures

Due to the large amount of videos generated through several data sources, the development of efficient mechanisms for storing, indexing, retrieving and visualizing their content is a challenging task. Temporal video segmentation is the automatic process of detecting transitions in video sequences, which is a fundamental step in the analysis of video content. This work proposes and evaluates an improved shot detection method based on the fusion of multiple frame dissimilarity measures and an adaptive threshold strategy. Experimental results demonstrate that the combination of different temporal features associated with an adequate threshold estimation can substantially improve the performance of individual methods.
A novel feature descriptor based on the shearlet transform

Problems such as image classification, object detection and recognition rely on low-level feature descriptors to represent visual information. Several feature extraction methods have been proposed, including the Histograms of Oriented Gradients (HOG), which captures edge information by analyzing the distribution of intensity gradients and their directions. In addition to directions, the analysis of edge at different scales provides valuable information. Shearlet transforms provide a general framework for analyzing and representing data with anisotropic information at multiple scales. As a consequence, signal singularities, such as edges, can be precisely detected and located in images. Based on the idea of employing histograms to estimate the distribution of edge orientations and on the accurate multi-scale analysis provided by shearlet transforms, we propose a feature descriptor called Histograms of Shearlet Coefficients (HSC). Experimental results comparing HOG with HSC show that HSC provides significantly better results for the problems of texture classification and face identification.
Video-Based Face Spoofing Detection through Visual Rhythm Analysis

Recent advances on biometrics, information forensics, and security have improved the accuracy of biometric systems, mainly those based on facial information. However, an ever-growing challenge is the vulnerability of such systems to impostor attacks, in which users without access privileges try to authenticate themselves as valid users. In this work, we present a solution to video-based face spoofing to biometric systems. Such type of attack is characterized by presenting a video of a real user to the biometric system. To the best of our knowledge, this is the first attempt of dealing with video-based face spoofing based in the analysis of global information that is invariant to video content. Our approach takes advantage of noise signatures generated by the recaptured video to distinguish between fake and valid access. To capture the noise and obtain a compact representation, we use the Fourier spectrum followed by the computation of the visual rhythm and extraction of the gray-level co-occurrence matrices, used as feature descriptors. Results show the effectiveness of the proposed approach to distinguish between valid and fake users for video-based spoofing with near-perfect classification results.
Real-time action recognition based on cumulative Motion shapes

Although several methods for action recognition have been proposed in the literature, many of them have limitations in terms of applicability in real-life situations. Despite satisfactory accuracy rates achieved by a number of methods, an effective action recognition system requires workability in real time. However, this feature usually comes along with certain loss in accuracy. In this paper, we present a real-time action recognition method that achieves state-of-the-art accuracy. By accumulating shape information over a sliding window on the video frames, the method extracts and processes silhouettes with little computational effort. Simple descriptors are computed over the shapes and applied on a fast configuration of classifiers. Experiments are conducted on three public data sets and the results demonstrate the effectiveness of the method in terms of accuracy and speed.
A hybrid genetic algorithm for image denoising

This paper presents a novel Hybrid Genetic Algorithm (HGA) for image denoising, whose main purpose is to restore images while preserving relevant information, for instance, texture and edges. The proposed method combines operators available in existing evolutionary methods, such as crossover, mutation and population reinitialization with some state-of-the-art image denoising methods. Experiments are conducted on a set of noise contaminated images commonly used by the scientific community as benchmark, where different levels of noise are applied to the images. The results achieved by the proposed method are compared against image denoising methods. The HGA performance demonstrated to be very effective and competitive, outperforming other approaches in several levels of noise.
An Approach to Iris Contact Lens Detection Based on Deep Image Representations

Spoofing detection is a challenging task in biometric systems, when differentiating illegitimate users from genuine ones. Although iris scans are far more inclusive than fingerprints, and also more precise for person authentication, iris recognition systems are vulnerable to spoofing via textured cosmetic contact lenses. Iris spoofing detection is also referred to as liveness detection (binary classification of fake and real images). In this work, we focus on a three-class detection problem: images with textured (colored) contact lenses, soft contact lenses, and no lenses. Our approach uses a convolutional network to build a deep image representation and an additional fully-connected single layer with soft max regression for classification. Experiments are conducted in comparison with a state-of-the-art approach (SOTA) on two public iris image databases for contact lens detection: 2013 Notre Dame and IIIT-Delhi. Our approach can achieve a 30% performance gain over SOTA on the former database (from 80% to 86%) and comparable results on the latter. Since IIIT-Delhi does not provide segmented iris images and, differently from SOTA, our approach does not segment the iris yet, we conclude that these are very promising results.
Exposing Digital Image Forgeries by Illumination Color Classification

For decades, photographs have been used to document space-time events and they have often served as evidence in courts. Although photographers are able to create composites of analog pictures, this process is very time consuming and requires expert knowledge. Today, however, powerful digital image editing software makes image modifications straightforward. This undermines our trust in photographs and, in particular, questions pictures as evidence for real-world events. In this paper, we analyze one of the most common forms of photographic manipulation, known as image composition or splicing. We propose a forgery detection method that exploits subtle inconsistencies in the color of the illumination of images. Our approach is machine-learning-based and requires minimal user interaction. The technique is applicable to images containing two or more people and requires no expert interaction for the tampering decision. To achieve this, we incorporate information from physics- and statistical-based illuminant estimators on image regions of similar material. From these illuminant estimates, we extract texture- and edge-based features which are then provided to a machine-learning approach for automatic decision-making. The classification performance using an SVM meta-fusion classifier is promising. It yields detection rates of 86% on a new benchmark dataset consisting of 200 images, and 83% on 50 images that were collected from the Internet.
Competition on counter measures to 2-D facial spoofing attacks

Spoofing identities using photographs is one of the most common techniques to attack 2-D face recognition systems. There seems to exist no comparative studies of different techniques using the same protocols and data. The motivation behind this competition is to compare the performance of different state-of-the-art algorithms on the same database using a unique evaluation method. Six different teams from universities around the world have participated in the contest. Use of one or multiple techniques from motion, texture analysis and liveness detection appears to be the common trend in this competition. Most of the algorithms are able to clearly separate spoof attempts from real accesses. The results suggest the investigation of more complex attacks.
A learning-based single-image super-resolution method for very low quality license plate images

Spatial resolution enhancement of license plate images in real scenarios plays an important role in the fields of criminal investigation and forensic science. This paper presents a learning-based single-image super-resolution method that uses a priori knowledge of the input as the plate images captured at poor quality and very low resolution. The proposed method employs a decision tree to classify the input image and the classification results are used to weight the image patches in the reconstruction step. Additionally, a histogram equalization is performed to improve the performance of the classifier. Experiments conducted on synthetic and real-world images demonstrate that the proposed method is capable of producing satisfactory results.
Image denoising based on genetic algorithm

Digital images play an essential role in analysis tasks with applications in various knowledge domains, such as medicine, meteorology, geology, biology, among others. Such images can be degraded by noise during the process of acquisition, transmission, storage or compression. Although several image denoising methods have been proposed in the literature, noise suppression in images still remains a challenging problem for researchers since the process can cause the removal of relevant image features, such as edges and corners. This papers describes a novel image denoising method based on a genetic algorithm. A population of noisy images is evolved for several epochs applying tailor-made crossover and mutation operators. The population is reinitialized every time a convergence occurs, when only the best individual (image) is kept for the next epoch. Experimental results demonstrate that the proposed method is competitive in comparison with state-of-the-art approaches.
Fitting smooth surfaces to scattered 3D data using piecewise quadratic approximation

The approximation of surfaces to scattered data is an important problem encountered in a variety of scientific applications, such as reverse engineering, computer vision, computer graphics, and terrain modeling. This paper describes an automatic method for constructing smooth surfaces defined as a network of curved triangular patches. The method starts with a coarse mesh approximating the surface through triangular elements covering the boundary of the domain, then iteratively adds new points from the data set until a specified error tolerance is achieved. The resulting surface over the triangular mesh is represented by piecewise polynomial patches possessing C/sup 1/ continuity. The method has been implemented and tested on a number of real data sets.
Efficient fusion of multidimensional descriptors for image retrieval

Due to the large diversity of existing feature descriptors in content-based image retrieval, the image contents can be better represented by the joint use of several descriptors in order to explore their potentially complementary characteristics. This paper presents and discusses a strategy for fusion of the different multidimensional features involved, based on inverted multi-indices and dedicated to similarity search. Image descriptors are quantized separately and efficiently through dimension reduction techniques, before being combined in the inverted multi-indices. To exhibit its effectiveness, the proposal is evaluated on two datasets having different contents and sizes, facing several state-of-the-art approaches of image descriptor fusion. The obtained results reconfirm that the joint use of several descriptions improves similarity search, and show that our fusion proposal outperforms other solutions, while manipulating lower or similar volumes of features.
Detection of Violent Events in Video Sequences Based on Census Transform Histogram

Video surveillance systems have enabled the monitoring of complex events in several places, such as airports, banks, streets, schools, industries, among others. Due to the massive amount of multimedia data acquired by video cameras, traditional visual inspection by human operators is a very tedious and time consuming task, whose performance is affected by fatigue and stress. A challenge is to develop intelligent video systems capable of automatically analyzing long sequences of videos from a large number of cameras. This work describes and evaluates the use of CENTRIST-based features to identify violence context from video scenes. Experimental results demonstrate the effectiveness of our method when applied to two public benchmarks, Violent Flows and Hockey Fights datasets.
Partial Least Squares Image Clustering

Clustering techniques have been widely used in areas that handle massive amounts of data, such as statistics, information retrieval, data mining and image analysis. This work presents a novel image clustering method called Partial Least Square Image Clustering (PLSIC), which employs a one against-all Partial Least Squares classifier to find image clusters with low redundancy (each cluster represents different visual concept) and high purity (two visual concepts should not be in the same cluster). The main goal of the proposed approach is to find groups of images in an arbitrary set of unlabeled images to convey well defined visual concepts. As a case study, we evaluate the PLSIC to the video summarization problem by means of experiments with 50 videos from various genres of the Open Video Project, comparing summaries generated by the PLSIC with other video summarization approaches found in the literature. A experimental evaluation demonstrates that the proposed method can produce very satisfactory results.
Improved Similarity Trees and their Application to Visual Data Classification

An alternative form to multidimensional projections for the visual analysis of data represented in multidimensional spaces is the deployment of similarity trees, such as Neighbor Joining trees. They organize data objects on the visual plane emphasizing their levels of similarity with high capability of detecting and separating groups and subgroups of objects. Besides this similarity-based hierarchical data organization, some of their advantages include the ability to decrease point clutter; high precision; and a consistent view of the data set during focusing, offering a very intuitive way to view the general structure of the data set as well as to drill down to groups and subgroups of interest. Disadvantages of similarity trees based on neighbor joining strategies include their computational cost and the presence of virtual nodes that utilize too much of the visual space. This paper presents a highly improved version of the similarity tree technique. The improvements in the technique are given by two procedures. The first is a strategy that replaces virtual nodes by promoting real leaf nodes to their place, saving large portions of space in the display and maintaining the expressiveness and precision of the technique. The second improvement is an implementation that significantly accelerates the algorithm, impacting its use for larger data sets. We also illustrate the applicability of the technique in visual data mining, showing its advantages to support visual classification of data sets, with special attention to the case of image classification. We demonstrate the capabilities of the tree for analysis and iterative manipulation and employ those capabilities to support evolving to a satisfactory data organization and classification.
Cross-Eyed - Cross-Spectral Iris/Periocular Recognition Database and Competition

This work presents a novel dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. This database was used in the 1st Cross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed 2016). This competition aimed at recording recent advances in cross- spectrum iris and periocular recognition. Six submissions were evaluated for cross-spectrum periocular recognition, and three for iris recognition. The submitted algorithms are briefly introduced. Detailed results are reported in this paper, and comparison of the results is discussed.
Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation

The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard, and moustache. We consider two assumptions: 1) manual estimation of soft biometrics and 2) automatic estimation from two commercial off-the-shelf systems (COTS). All experiments are reported using the labeled faces in the wild (LFW) database. First, we study the discrimination capabilities of soft biometrics standalone. Then, experiments are carried out fusing soft biometrics with two state-of-the-art face recognition systems based on deep learning. We observe that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometrics estimation. Results are reproducible as we make public our manual annotations and COTS outputs of soft biometrics over LFW, as well as the face recognition scores.
Impact of time variability in off-line writer identification and verification

One of the biggest challenges in person recognition using biometric systems is the variability in the acquired data. In this paper, we evaluate the effects of an increasing time lapse between reference and test biometric data consisting of static images of handwritten signatures and texts. We use for our experiments two recognition approaches exploiting information at the global and local levels, and the BiosecurlD database, containing 3,724 signature images and 532 texts of 133 individuals acquired in four acquisition sessions distributed along a 4 months time span. We report results of the recognition systems working both in verification (one-to-one) and identification (one-to-many) mode. The results show the extent of the impact that the time separation between samples under comparison has on the recognition rates, being the local approach more robust to the time lapse than the global one. We also observe in our experiments that recognition based on handwritten texts provides higher accuracy than recognition based on signatures.
Comparison and fusion of multiple iris and periocular matchers using near-infrared and visible images

Periocular refers to the facial region in the eye vicinity. It can be easily obtained with existing face and iris setups, and it appears in iris images, so its fusion with the iris texture has a potential to improve the overall recognition. It is also suggested that iris is more suited to near-infrared (NIR) illumination, whereas the periocular modality is best for visible (VW) illumination. Here, we evaluate three periocular and three iris matchers based on different features. As experimental data, we use five databases, three acquired with a close-up NIR camera, and two in VW light with a webcam and a digital camera. We observe that the iris matchers perform better than the periocular matchers with NIR data, and the opposite with VW data. However, in both cases, their fusion can provide additional performance improvements. This is specially relevant with VW data, where the iris matchers perform significantly worse (due to low resolution), but they are still able to complement the periocular modality.