nils-herrmann/clean_scirepeval huggingface dataset:
Bayesian Feedback in Data Clustering

In many clustering applications, the user has some vague notion of the number and membership of the desired clusters. However, it is difficult for the user to provide such knowledge explicitly in the clustering process. We propose a solution to circumvent this difficulty by introducing a feedback mechanism. The notion of Bayesian inference for relevance feedback in content-based image retrieval is modified for data clustering. Given the number of clusters, the proposed algorithm seeks information about the target partition by asking the user a sequence of queries about whether a pair of objects should be put in the same cluster or not. Information-theoretic criteria is adopted to select the queries to be presented to the user. The assumption made here is that cluster labels are "smooth", i.e., similar objects should share the same cluster labels. We show that it is possible to obtain reasonable partitions based on the user feedback alone, without the need of specifying a clustering objective function
Feature Selection: Evaluation, Application, and Small Sample Performance

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection algorithm, proposed by Pudil et al. (1994), dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. We also illustrate the dangers of using feature selection in small sample size situations.
Heterogeneous Face Recognition: Matching NIR to Visible Light Images

Matching near-infrared (NIR) face images to visible light (VIS) face images offers a robust approach to face recognition with unconstrained illumination. In this paper we propose a novel method of heterogeneous face recognition that uses a common feature-based representation for both NIR images as well as VIS images. Linear discriminant analysis is performed on a collection of random subspaces to learn discriminative projections. NIR and VIS images are matched (i) directly using the random subspace projections, and (ii) using sparse representation classification. Experimental results demonstrate the effectiveness of the proposed approach for matching NIR and VIS face images.
Tag Completion for Image Retrieval

Many social image search engines are based on keyword/tag matching. This is because tag-based image retrieval (TBIR) is not only efficient but also effective. The performance of TBIR is highly dependent on the availability and quality of manual tags. Recent studies have shown that manual tags are often unreliable and inconsistent. In addition, since many users tend to choose general and ambiguous tags in order to minimize their efforts in choosing appropriate words, tags that are specific to the visual content of images tend to be missing or noisy, leading to a limited performance of TBIR. To address this challenge, we study the problem of tag completion, where the goal is to automatically fill in the missing tags as well as correct noisy tags for given images. We represent the image-tag relation by a tag matrix, and search for the optimal tag matrix consistent with both the observed tags and the visual similarity. We propose a new algorithm for solving this optimization problem. Extensive empirical studies show that the proposed algorithm is significantly more effective than the state-of-the-art algorithms. Our studies also verify that the proposed algorithm is computationally efficient and scales well to large databases.
Grouper: Optimizing Crowdsourced Face Annotations

This study focuses on the problem of extracting consistent and accurate face bounding box annotations from crowdsourced workers. Aiming to provide benchmark datasets for facial recognition training and testing, we create a 'gold standard' set against which consolidated face bounding box annotations can be evaluated. An evaluation methodology based on scores for several features of bounding box annotations is presented and is shown to predict consolidation performance using information gathered from crowdsourced annotations. Based on this foundation, we present "Grouper," a method leveraging density-based clustering to consolidate annotations by crowd workers. We demonstrate that the proposed consolidation scheme, which should be extensible to any number of region annotation consolidations, improves upon metadata released with the IARPA Janus Benchmark-A. Finally, we compare FR performance using the originally provided IJB-A annotations and Grouper and determine that similarity to the gold standard as measured by our evaluation metric does predict recognition performance.
3D object recognition: interpretation tree search on a MIMD machine

This paper presents a parallel implementation of a tree search strategy for recognition of 3D objects in range images. A coarse-grained and a medium-grained implementaton of the tree search algorithm is presented and analyzed. The performances of the two implementations are analyzed on a 96-node BBN GP-1000 and on a 48-node BBN TC-2000; and, for comparison, on a Sun SPARCstation 2.<<ETX>>
Latent Fingerprint Matching: Performance Gain via Feedback from Exemplar Prints

Latent fingerprints serve as an important source of forensic evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, latent impressions are typically of poor quality with complex background noise which makes feature extraction and matching of latents a significantly challenging problem. We propose incorporating top-down information or feedback from an exemplar to refine the features extracted from a latent for improving latent matching accuracy. The refined latent features (e.g. ridge orientation and frequency), after feedback, are used to re-match the latent to the top K candidate exemplars returned by the baseline matcher and resort the candidate list. The contributions of this research include: (i) devising systemic ways to use information in exemplars for latent feature refinement, (ii) developing a feedback paradigm which can be wrapped around any latent matcher for improving its matching performance, and (iii) determining when feedback is actually necessary to improve latent matching accuracy. Experimental results show that integrating the proposed feedback paradigm with a state-of-the-art latent matcher improves its identification accuracy by 0.5-3.5 percent for NIST SD27 and WVU latent databases against a background database of 100k exemplars.
Efficient Kernel Clustering Using Random Fourier Features

Kernel clustering algorithms have the ability to capture the non-linear structure inherent in many real world data sets and thereby, achieve better clustering performance than Euclidean distance based clustering algorithms. However, their quadratic computational complexity renders them non-scalable to large data sets. In this paper, we employ random Fourier maps, originally proposed for large scale classification, to accelerate kernel clustering. The key idea behind the use of random Fourier maps for clustering is to project the data into a low-dimensional space where the inner product of the transformed data points approximates the kernel similarity between them. An efficient linear clustering algorithm can then be applied to the points in the transformed space. We also propose an improved scheme which uses the top singular vectors of the transformed data matrix to perform clustering, and yields a better approximation of kernel clustering under appropriate conditions. Our empirical studies demonstrate that the proposed schemes can be efficiently applied to large data sets containing millions of data points, while achieving accuracy similar to that achieved by state-of-the-art kernel clustering algorithms.
Discriminant analysis neural networks

An artificial neural network and a supervised self-organizing learning algorithm for multivariate linear discriminant analysis are proposed. The precision of the neural computation is shown to be high enough for feature selection and projection purposes. A nonlinear discriminant analysis network (supervised nonlinear projection method) based on the multilayer feedforward network is also suggested. A comparative study of the principal component analysis network, linear discriminant analysis network, and nonlinear discriminant analysis network based on three criteria on various data sets is provided. A significance advantage of these neural networks over conventional approaches is their plasticity, which allows the networks to adapt themselves to new input data.<<ETX>>
Data clustering using evidence accumulation

We explore the idea of evidence accumulation for combining the results of multiple clusterings. Initially, n d-dimensional data is decomposed into a large number of compact clusters; the K-means algorithm performs this decomposition, with several clusterings obtained by N random initializations of the K-means. Taking the co-occurrences of pairs of patterns in the same cluster as votes for their association, the data partitions are mapped into a co-association matrix of patterns. This n/spl times/n matrix represents a new similarity measure between patterns. The final clusters are obtained by applying a MST-based clustering algorithm on this matrix. Results on both synthetic and real data show the ability of the method to identify arbitrary shaped clusters in multidimensional data.
Automatic filter design for texture discrimination

Multichannel filtering has been shown by many researchers to provide good features for texture segmentation and classification. In this paper the authors exploit neural networks to construct optimal filters and to combine the outputs of these filters for the classification of known textures. The authors use the neural network training together with node pruning, so that both the classification error and the number of filters or, equivalently, the number of features, are minimized. The performance of the neural network classifier is demonstrated an several experiments involving classification of natural textures. The authors study the effects of using different sized filters with different network configurations. The authors show that the number of filters, and, therefore, the processing time, can be greatly reduced while preserving the classification accuracy, using the proposed scheme compared to using a general set of filters (e.g., Gabor filters).
Active query selection for semi-supervised clustering

Semi-supervised clustering allows a user to specify available prior knowledge about the data to improve the clustering performance. A common way to express this information is in the form of pair-wise constraints. A number of studies have shown that, in general, these constraints improve the resulting data partition. However, the choice of constraints is critical since improperly chosen constraints might actually degrade the clustering performance. We focus on constraint (also known as query) selection for improving the performance of semi-supervised clustering algorithms. We present an active query selection mechanism, where the queries are selected using a min-max criterion. Experimental results on a variety of datasets, using MPCK-means as the underlying semi-clustering algorithm, demonstrate the superior performance of the proposed query selection procedure.
Continuous authentication of mobile user: Fusion of face image and inertial Measurement Unit data

Mobile devices can carry large amounts of personal data, but are often left unsecured. PIN locks are inconvenient to use and thus have seen low adoption (33% of users). While biometrics are beginning to be used for mobile device authentication, they are used only for initial unlock. Mobile devices secured with only login authentication are still vulnerable to data theft when in an unlocked state. This paper introduces our work on a face-based continuous authentication system that operates in an unobtrusive manner. We present a methodology for fusing mobile device (unconstrained) face capture with gyroscope, accelerometer, and magnetometer data to correct for camera orientation and, by extension, the orientation of the face image. Experiments demonstrate (i) improvement of face recognition accuracy from face orientation correction, and (ii) efficacy of the prototype continuous authentication system.
COSMOS-a representation scheme for free-form surfaces

We address the problem of representing and recognizing arbitrarily curved 3D rigid objects when: the objects may vary in shape and complexity, and no restrictive assumptions are made about the types of surfaces on the object. We propose a new and general surface representation scheme for recognizing objects with free form (sculpted) surfaces from range data. In this scheme, an object is described concisely in terms of maximal surface patches of constant shape index. These maximal patches are mapped onto the unit sphere via their orientations, and aggregated via shape spectral functions. Properties such as surface area, curvedness and connectivity that capture local and global information are also built into the representation. The scheme yields not only a meaningful and rich surface description useful for the recoverability of the object, but also a set of powerful indexing primitives for object matching. We demonstrate the generality and the effectiveness of our scheme using real range images of complex objects. We also present results on the categorization of object views based on a novel shape spectral matching technique.<<ETX>>
Detection of Anchor Points for 3D Face Veri.cation

This paper outlines methods to detect key anchor points in 3D face scanner data. These anchor points can be used to estimate the pose and then match the test image to a 3D face model. We present two algorithms for detecting face anchor points in the context of face verification; One for frontal images and one for arbitrary pose. We achieve 99% success in finding anchor points in frontal images and 86% success in scans with large variations in pose and changes in expression. These results demonstrate the challenges in 3D face recognition under arbitrary pose and expression. We are currently working on robust ?tting algorithms to localize more precisely the anchor points for arbitrary pose images.
Secure interleaving - physical layer built-in security enhancement of CDMA systems

Generally the physical layer of a communication system does not possess built-in security features except the spread spectrum systems. However, the physical layer built-in in-formation privacy of the conventional CDMA system, provided by pseudo-random scrambling, is far from adequate and can be improved by applying cryptographic techniques in the scrambling process. Motivated by the fact that after scrambling, chips spread from one symbol still cluster together and could be fragile to strong burst errors and fading effects, in this paper, a chip-level secure interleaving procedure is proposed to improve the system performance while enhancing the security measure. More specifically, the AES algorithm is combined with block interleaving. It should be noted that interleaving is in fact a special case of scrambling. Security analysis is provided to demonstrate the effectiveness of the proposed secure interleaving scheme under exhaustive search attack. Simulation examples are presented to illustrate the robustness of chip-level interleaving over channels with severe fading or strong burst errors
A Generic System for Form Dropout

Recent advances in intelligent character recognition are enabling us to address many challenging problems in document image analysis. One of them is intelligent form analysis. This paper describes a generic system for form dropout when the filled-in characters or symbols are either touching or crossing the form frames. We propose a method to separate these characters from form frames whose locations are unknown. Since some of the character strokes are either touching or crossing the form frames, we need to address the following three issues: 1) localization of form frames; 2) separation of characters and form frames; and 3) reconstruction of broken strokes introduced during separation. The form frame is automatically located by finding long straight lines based on the block adjacency graph. Form frame separation and character reconstruction are implemented by means of this graph. The proposed system includes form structure learning and form dropout. First, a form structure-based template is automatically generated from a blank form which includes form frames, preprinted data areas and skew angle. With this form template, our system can then extract both handwritten and machine-typed filled-in data. Experimental results on three different types of forms show the performance of our system. Further, the proposed method is robust to noise and skew that is introduced during scanning.
Face modeling for recognition

3D human face models have been widely used in applications such as facial animation, video compression/coding, augmented reality, head tracking, facial expression recognition, human action recognition, and face recognition. Modeling human faces provides a potential solution to identifying faces with variations in illumination, pose, and facial expression. We propose a method of modeling human faces based on a generic face model (a triangular mesh model) and individual facial measurements containing both shape and texture information. The modeling method adapts a generic face model to the given facial features, extracted from registered range and color images, in a global-to-local fashion. It iteratively moves the vertices of the mesh model to smoothen the non-feature areas, and uses the 2.5D active contours to refine feature boundaries. The resultant face model has been shown to be visually similar to the true face. Initial results show that the constructed model is quite useful for recognizing nonfrontal views.
Deformation Modeling for Robust 3D Face Matching

Human face recognition based on 3D surface matching is promising for overcoming the limitations of current 2D image-based face recognition systems. The 3D shape is invariant to the pose and lighting changes, but not invariant to the non-rigid facial movement, such as expressions. Collecting and storing multiple templates for each subject in a large database (associated with various expressions) is not practical. We present a facial surface modeling and matching scheme to match 2.5D test scans in the presence of both non-rigid deformations and large pose changes (multiview) to a neutral expression 3D face model. A geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A personspecific (3D) deformable model is built for each subject in the gallery w.r.t. the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and large pose changes simultaneously. Experimental results demonstrate that the proposed matching scheme based on deformation modeling improves the matching accuracy.
Markov face models

The spatial distribution of gray level intensities in an image can be naturally modeled using Markov random field (MRF) models. We develop and investigate the performance of face detection algorithms derived from MRF considerations. For enhanced detection, the MRF models are defined for every permutation of site indices (pixels) in the image. We find the optimal permutation that provides maximum discriminatory power to identify faces from nonfaces. The methodology presented here is a generalization of the face detection algorithm described previously where a most discriminating Markov chain model was used. The MRF models successfully detect faces in a number of test images.
Smart Automotive Airbags: Occupant Classification and Tracking

The introduction of airbags into automobiles has significantly improved the safety of the occupants. Unfortunately, airbags can also cause fatal injuries if the occupant is a child smaller (in weight) than a typical six-year-old. Between 1986 and 2001, 19 infants and 85 children were killed by airbags during relatively minor vehicle collisions. In addition to these infant and child deaths, there have also been seven adults killed by airbags due to their proximity to the airbag during deployment. In response to these deaths, the National Highway Transportation and Safety Administration has mandated that, starting in the 2006 model year, all automobiles be equipped with automatic airbag suppression. The suppression of the airbag based on the type of occupant can be framed as a two-class classification problem, while the suppression of the airbag based on the location of the occupant relative to the airbag can be framed as an occupant-tracking problem. This paper describes an integrated real-time vision-based occupant classification and tracking system using a single grayscale camera with commercially available processing hardware. The classification system has achieved a classification accuracy of approximately 98%. Likewise, the tracking system has demonstrated the ability to detect a dangerous proximity of the occupant relative to the airbag within only 7 ms
Locating text in complex color images

There is a substantial interest in retrieving images from a large database using the textual information contained in the images. An algorithm which will automatically locate the textual regions in the input image will facilitate this task; the optical character recognizer can then be applied to only those regions of the image which contain text. We present a method for automatically locating text in complex color images. The algorithm first finds the approximate locations of text lines using horizontal spatial variance, and then extracts text components in these boxes using color segmentation. The proposed method has been used to locate text in compact disc (CD) and book cover images, as well as in the images of traffic scenes captured by a video camera. Initial results are encouraging and suggest that these algorithms can be used in image retrieval applications.