nils-herrmann/clean_scirepeval huggingface dataset:
Generalized Sobel Filters for gradient estimation of distorted images

In this paper we tackle the problem of correctly estimating the gradient of distorted images. The proper estimation of the gradient in the presence of distortion is of great interest due to the large number of applications relying on wide angle cameras (e.g., in surveillance, automotive, robotics). To this aim we propose the Generalized Sobel Filters (GSF), a family of adaptive Sobel filters able to correctly estimate the gradient of distorted images. To assess the performances of the proposed method, we acquired a benchmark dataset of high resolution images belonging to different categories which are relevant to application domains where the gradient estimation is usually employed. We build an objective evaluation pipeline and perform experiments which show that our method outperforms the state-of-the-art.
Gender recognition: Methods, datasets and results

Digital Out Of Home (DOOH) applications which exploit computer vision algorithms to automatically collect soft biometrics of people in front a smart screen are of great interest for industry. In the last years many gender recognition pipelines have been proposed in literature. Different benchmark datasets have been introduced and used for testing purpose. This paper gives an overview of the state-of-the-art in the context of gender recognition by highlighting features, classifiers and datasets which can be employed to reach the goal. Comparisons of the results obtained by different approaches are also presented.
Selecting discriminative CLBP patterns for age estimation

Face analysis is of great interest in the context of digital signage to understand soft biometric of a person. Among the others information gathered from a face, the age of a person is still an open challenging problem. Face representation takes an important role for real time age discrimination. LBP descriptor and the related variants (e.g., CLBP) have been demonstrated to obtain the state-of-the-art performances in this field. In this paper, building on the CLBP representation, we propose a method to select the most discriminative CLBP patterns to represent faces for age classification. Experiments confirm that the proposed method improves the age classification accuracy by reducing computational costs both in terms of space and time.
Saliency-Based Selection of Gradient Vector Flow Paths for Content Aware Image Resizing

Content-aware image resizing techniques allow to take into account the visual content of images during the resizing process. The basic idea beyond these algorithms is the removal of vertical and/or horizontal paths of pixels (i.e., seams) containing low salient information. In this paper, we present a method which exploits the gradient vector flow (GVF) of the image to establish the paths to be considered during the resizing. The relevance of each GVF path is straightforward derived from an energy map related to the magnitude of the GVF associated to the image to be resized. To make more relevant, the visual content of the images during the content-aware resizing, we also propose to select the generated GVF paths based on their visual saliency properties. In this way, visually important image regions are better preserved in the final resized image. The proposed technique has been tested, both qualitatively and quantitatively, by considering a representative data set of 1000 images labeled with corresponding salient objects (i.e., ground-truth maps). Experimental results demonstrate that our method preserves crucial salient regions better than other state-of-the-art algorithms.
How Shall We Evaluate Egocentric Action Recognition?

Egocentric action analysis methods often assume that input videos are trimmed and hence they tend to focus on action classification rather than recognition. Consequently, adopted evaluation schemes are often unable to assess important properties of the desired action video segmentation output, which are deemed to be meaningful in real scenarios (e.g., oversegmentation and boundary localization precision). To overcome the limits of current evaluation methodologies, we propose a set of measures aimed to quantitatively and qualitatively assess the performance of egocentric action recognition methods. To improve exploitability of current action classification methods in the recognition scenario, we investigate how frame-wise predictions can be turned into action-based temporal video segmentations. Experiments on both synthetic and real data show that the proposed set of measures can help to improve evaluation and to drive the design of egocentric action recognition methods.
Scene categorization using bag of Textons on spatial hierarchy

This paper proposes a method to recognize scene categories using bags of visual words obtained hierarchically partitioning into subregion the input images. Specifically, for each subregions the texton histogram and the extension of the sub-region is taken into account. The bags of visual words, obtained in this way, are weighted and used in a similarity measure during the categorization. Experimental tests using ten different scene categories show that the proposed approach achieves good performances with respect to the state of the art methods.
Affine region detectors on the fisheye domain

Feature extractors play an important role in different Computer Vision application domains such as registration, recognition and visual search. Different detectors have been proposed and evaluated so far assuming images taken with classic cameras. However, many operating cameras (e.g., in surveillance and automotive) are built considering a fisheye model and a preprocessing step is performed to remove the distortion of the images before running a detector. The following question arises: are the current detectors suitable to work directly in the fisheye domain? To answer this question, in this paper a benchmark dataset and objective evaluation measures are considered to evaluate the performances of the state-of-the-art detectors in the fisheye domain. Test images are properly generated starting from benchmark rectilinear images and considering different fisheye focal lengths. The experiments evaluate the performances of the detectors against both increasing fisheye distortion and the combination of the fisheye distortion with photometric and geometric variability of the image content. The experiments demonstrate that affine covariant detectors can be employed directly in the fisheye domain. Furthermore, although the transformation between the rectilinear and the fisheye coordinates is not affine, we show that the mapping can be locally approximated by linear functions with a small error.
Scene classification in compressed and constrained domain

Holistic representations of natural scenes are an effective and powerful source of information for semantic classification and analysis of images. Despite the technological hardware and software advances, consumer single-sensor imaging devices technology are quite far from the ability of recognising scenes and/or to exploit the visual content during (or after) acquisition time. The frequency domain has been successfully exploited to holistically encode the content of natural scenes in order to obtain a robust representation for scene classification. The authors exploit a holistic representation of the scene in the discrete cosine transform domain fully compatible with the JPEG format. The advised representation is coupled with a logistic classifier to perform classification of the scene at superordinate level of description (e.g. natural against artificial), or to discriminate between multiple classes of scenes usually acquired by a consumer imaging device (e.g. portrait, landscape and document). The proposed method is able to work in constrained domain. Experiments confirm the effectiveness of the proposed method. The obtained results closely match state-of-the-art methods in terms of accuracy outperforming in terms of computational resources.
Recognizing Personal Locations From Egocentric Videos

Contextual awareness in wearable computing allows for construction of intelligent systems, which are able to interact with the user in a more natural way. In this paper, we study how personal locations arising from the user's daily activities can be recognized from egocentric videos. We assume that few training samples are available for learning purposes. Considering the diversity of the devices available on the market, we introduce a benchmark dataset containing egocentric videos of eight personal locations acquired by a user with four different wearable cameras. To make our analysis useful in real-world scenarios, we propose a method to reject negative locations, i.e., those not belonging to any of the categories of interest for the end-user. We assess the performances of the main state-of-the-art representations for scene and object classification on the considered task, as well as the influence of device-specific factors such as the field of view and the wearing modality. Concerning the different device-specific factors, experiments revealed that the best results are obtained using a head-mounted wide-angular device. Our analysis shows the effectiveness of using representations based on convolutional neural networks, employing basic transfer learning techniques and an entropy-based rejection algorithm.
Boosting Gray Codes for Red Eyes Removal

Since the large diffusion of digital camera and mobile devices with embedded camera and flashgun, the red-eyes artifacts have de-facto become a critical problem. The technique herein described makes use of three main steps to identify and remove red-eyes. First, red eyes candidates are extracted from the input image by using an image filtering pipeline. A set of classifiers is then learned on gray code features extracted in the clustered patches space, and hence employed to distinguish between eyes and non-eyes patches. Once red-eyes are detected, artifacts are removed through desaturation and brightness reduction. The proposed method has been tested on large dataset of images achieving effective results in terms of hit rates maximization, false positives reduction and quality measure.
PCA based shape recognition for capacitive touch display

With the growing diffusion of touch screen based consumer devices, the development of algorithms able to discriminate among different shapes obtained by touching the display becomes very important. For instance, the detection and recognition of fingers represent fundamental information in many touch based user applications such as image zooming, swipe, figure rotation, gaming, finger painting, etc. These algorithms are also extremely useful to recognize accidental touches in order to avoid involuntary activation of the device functionalities. Moreover, the recognition engine should be able to identify shapes acquired in critical conditions (e.g. slightly wet fingers). In this paper we present both hardware and software components to recognize different classes of shapes: single finger, double fingers and palm.
Content-aware image resizing with seam selection based on Gradient Vector Flow

Content-aware image resizing is an effective technique that allows to take into account the visual content of images during the resizing process. The basic idea beyond these algorithms is the resizing of an image by considering vertical and/or horizontal paths of pixels (i.e., seams) which contain low salient information. In this paper we exploit the Gradient Vector Flow (GVF) of the image to establish the paths to be considered during the resizing. The relevance of each path is derived from a saliency map obtained by considering the magnitude of the GVF associated to the image under consideration. The proposed technique has been tested, both qualitatively and quantitatively, by considering a representative set of images labeled with corresponding salient objects (i.e., ground-truth maps). Experimental results demonstrate that our method preserves crucial salient regions better than other state-of-the-art algorithms.
Understanding geometric manipulations of images through bovw-based hashing

The increasing use of low cost imaging devices and the innovations in terms of media distribution technologies induce a growing interest on technologies able to protect digital visual media against malicious manipulations of the visual contents. One of the main problems addressed in this research area is the blind detection of traces of forgery on an image obtained through the internet. Specifically, in this paper we consider the context of communications, where malicious image manipulations should be detected by a receiver. In the proposed method, an image hash based on the Bag of Visual Words paradigm is attached as signature to the image before transmission. The forensic hash is then analyzed at destination to detect the geometric transformations which have been applied to the received image. This task is fundamental for further processing which usually assumes that the received image is aligned with the original one, as in the case of tampering detection systems. Experiments show that the proposed approach outperforms state-of-the art methods by obtaining a good margin in terms of performances.
Classifying food images represented as Bag of Textons

The classification of food images is an interesting and challenging problem since the high variability of the image content which makes the task difficult for current state-of-the-art classification methods. The image representation to be employed in the classification engine plays an important role. We believe that texture features have been not properly considered in this application domain. This paper points out, through a set of experiments, that textures are fundamental to properly recognize different food items. For this purpose the bag of visual words model (BoW) is employed. Images are processed with a bank of rotation and scale invariant filters and then a small codebook of Textons is built for each food class. The learned class-based Textons are hence collected in a single visual dictionary. The food images are represented as visual words distributions (Bag of Textons) and a Support Vector Machine is used for the classification stage. The experiments demonstrate that the image representation based on Bag of Textons is more accurate than existing (and more complex) approaches in classifying the 61 classes of the Pittsburgh Fast-Food Image Dataset.
Guest Editorial Nutrition Informatics: From Food Monitoring to Dietary Management

The papers in this special section address the concept of nutrition informatics from food monitoring to dietary management. Non-communicable diseases (NCD) account for a massively increasing proportion of the global health burden. A number of behavioral and physiological factors are related to the rising onset of NCD worldwide, with unhealthy eating playing a key role among them. In parallel, food allergies and associated acute and sometimes life-threatening reactions are a public health problem. Thus, balanced nutrition with a proper diet is the key to the prevention of diet related diseases. The recent advances in smartphone technologies, wearable sensors, computer vision and machine learning will bring the applications of nutrition informatics closer to the individuals and enable them to make better decisions regarding their daily lives.
Robust Image Alignment for Tampering Detection

The widespread use of classic and newest technologies available on Internet (e.g., emails, social networks, digital repositories) has induced a growing interest on systems able to protect the visual content against malicious manipulations that could be performed during their transmission. One of the main problems addressed in this context is the authentication of the image received in a communication. This task is usually performed by localizing the regions of the image which have been tampered. To this aim the aligned image should be first registered with the one at the sender by exploiting the information provided by a specific component of the forensic hash associated to the image. In this paper we propose a robust alignment method which makes use of an image hash component based on the Bag of Features paradigm. The proposed signature is attached to the image before transmission and then analyzed at destination to recover the geometric transformations which have been applied to the received image. The estimator is based on a voting procedure in the parameter space of the model used to recover the geometric transformation occurred into the manipulated image. The proposed image hash encodes the spatial distribution of the image features to deal with highly textured and contrasted tampering patterns. A block-wise tampering detection which exploits an histograms of oriented gradients representation is also proposed. A non-uniform quantization of the histogram of oriented gradient space is used to build the signature of each image block for tampering purposes. Experiments show that the proposed approach obtains good margin of performances with respect to state-of-the art methods.
Frontal Face Authentication Using Discriminating Grids with Morphological Feature Vectors

A novel elastic graph matching procedure based on multiscale morphological operations, the so called morphological dynamic link architecture, is developed for frontal face authentication. Fast algorithms for implementing mathematical morphology operations are presented. Feature selection by employing linear projection algorithms is proposed. Discriminatory power coefficients that weigh the matching error at each grid node are derived. The performance of morphological dynamic link architecture in frontal face authentication is evaluated in terms of the receiver operating characteristic on the M2VTS face image database. Preliminary results for face recognition using the proposed technique are also presented.
Symmetric Subspace Learning for Image Analysis

Subspace learning (SL) is one of the most useful tools for image analysis and recognition. A large number of such techniques have been proposed utilizing a priori knowledge about the data. In this paper, new subspace learning techniques are presented that use symmetry constraints in their objective functions. The rational behind this idea is to exploit the a priori knowledge that geometrical symmetry appears in several types of data, such as images, objects, faces, and so on. Experiments on artificial, facial expression recognition, face recognition, and object categorization databases highlight the superiority and the robustness of the proposed techniques, in comparison with standard SL techniques.
Laplacian one class extreme learning machines for human action recognition

A novel OCC method for human action recognition namely the Laplacian One Class Extreme Learning Machines is presented. The proposed method exploits local geometric data information within the OC-ELM optimization process. It is shown that emphasizing on preserving the local geometry of the data leads to a regularized solution, which models the target class more efficiently than the standard OC-ELM algorithm. The proposed method is extended to operate in feature spaces determined by the network hidden layer outputs, as well as in ELM spaces of arbitrary dimensions. Its superior performance against other OCC options is consistent among five publicly available human action recognition datasets.
3D image watermarking robust to geometric distortions

A novel blind method for 3D image watermarking robust against geometric distortions is proposed. A ternary watermark is embedded in a grayscale or a color 3D volume. Construction of watermarks having appropriate structure enables fast and robust watermark detection even after several geometric distortions of the watermarked volume. Simulation results indicate the ability of the proposed method to deal with the aforementioned attacks. The proposed method is also robust against lossy compression up to a certain compression ratio. Experiments conducted indicate the superiority of the proposed method.
Entropy Optimized Feature-Based Bag-of-Words Representation for Information Retrieval

In this paper, we present a supervised dictionary learning method for optimizing the feature-based Bag-of-Words (BoW) representation towards Information Retrieval. Following the cluster hypothesis, which states that points in the same cluster are likely to fulfill the same information need, we propose the use of an entropy-based optimization criterion that is better suited for retrieval instead of classification. We demonstrate the ability of the proposed method, abbreviated as EO-BoW, to improve the retrieval performance by providing extensive experiments on two multi-class image datasets. The BoW model can be applied to other domains as well, so we also evaluate our approach using a collection of 45 time-series datasets, a text dataset, and a video dataset. The gains are three-fold since the EO-BoW can improve the mean Average Precision, while reducing the encoding time and the database storage requirements. Finally, we provide evidence that the EO-BoW maintains its representation ability even when used to retrieve objects from classes that were not seen during the training.
Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification

A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of weighted piecewise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than the one produced by the traditional linear discriminant analysis (LDA) methodology. The expected classification ability of this method is investigated throughout a series of simulations. The second phase defines proper combinations for person-specific similarity scores and describes an outlier removal process that further enhances the classification ability. The proposed technique has been tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the face-verification performance
Random Walk Kernel Applications to Classification Using Support Vector Machines

Kernel Methods are algorithms that are widely used, mainly because they can implicitly perform a non-linear mapping of the input data to a high dimensional feature space. In this paper, novel Kernel Matrices, that reflect the general structure of data, are proposed for classification. The proposed Matrices exploit properties of the graph theory, which are generated using power iterations of already known Kernel Matrices and three approaches are presented. Experiments on various datasets are conducted and statistical tests are performed, comparing our proposed approach against current Kernel Matrices used on support vector machines. Also, experiments on real datasets for folk dance and activity recognition that highlight the superiority of our proposed method, are provided.