nils-herrmann/clean_scirepeval huggingface dataset:
Bayesian modeling of video editing and structure: semantic features for video summarization and browsing

The ability to model content semantics is an important step towards the development of intelligent interfaces to large image and video databases. While an extremely difficult problem in the abstract, semantic characterization is possible in domains where a significant amount of structure is exhibited by the content. Whenever this is the case, given their ability to integrate prior knowledge about this structure in the inferences to be made, Bayesian methods are a natural solution to the problem. We present a Bayesian architecture for content characterization and analyze its potential as a tool for accessing and browsing through video databases on a semantic basis.
Embedded mixture modeling for efficient probabilistic content-based indexing and retrieval

By formulating content-based retrieval as a problem of Bayesian inference we have previously developed a retrieval framework with various interesting properties: (1) allows the incorporation of prior beliefs about image relevance in the retrieval process, (2) leads to simple and intuitive mechanisms for combining information from several modalities, such as images, audio, and text during retrieval, (3) provides support for the development of interfaces that learn from user interaction, (4) allows retrieval directly from compressed bitstreams, and (5) lends itself to the construction of indexing structures which can also be computed as a side effect of the compression process.
Multiresolution Tangent Distance for Affine-invariant Classification

The ability to rely on similarity metrics invariant to image transformations is an important issue for image classification tasks such as face or character recognition. We analyze an invariant metric that has performed well for the latter - the tangent distance - and study its limitations when applied to regular images, showing that the most significant among these (convergence to local minima) can be drastically reduced by computing the distance in a multiresolution setting. This leads to the multi resolution tangent distance, which exhibits significantly higher invariance to image transformations, and can be easily combined with robust estimation procedures.
Pre and post-filtering for low bit-rate video coding

We propose pre and post-filters for low bit rate video coding. The purpose of the former is to make the video sequence easier to encode, whereas the latter aims to remove coding artifacts. The proposed techniques are computationally efficient and lead to scalable architectures which (1) can handle varied types of video content and (2) can be adapted to the available bandwidth and computational resources. Simulation results using H.263 show that significant gains can be achieved by pre and post-filtering.
A Bayesian video modeling framework for shot segmentation and content characterization

The segmentation of video streams into their component shots is a pre-requisite for most applications involving content-based access to video libraries. In this paper, we address the segmentation problem from a probabilistic standpoint which exposes the major limitations of current solutions, and we suggest better alternatives derived from Bayesian principles. These principles lead to a framework which, by allowing the incorporation of prior knowledge about the video structure in its statistical model, leads to higher segmentation accuracy and provides a basis for content characterization which can later be used to categorize the video, retrieve it according to its content, or compare it to other instances stored in a database
Towards semantically meaningful feature spaces for the characterization of video content

Efficient procedures for browsing, filtering, sorting or retrieving pictorial content require accurate content characterization. Of particular interest are representations based on semantically meaningful feature spaces, capable of capturing properties such as violence, sex or profanity. In this work we report on a first step towards this goal, the design of a stochastic model for video editing which provides a transformation from the image space to a low-dimensional feature space where categorization by degree of action can be easily accomplished.
Library-based coding: a representation for efficient video compression and retrieval

The ubiquity of networking and computational capacity associated with the new communications media unveil a universe of new requirements for image representation. Among such requirements is the ability of the representation used for coding to support higher-level tasks such as content-based retrieval. We explore the relationships between probabilistic modeling and data compression to introduce a representation-library-based coding-which, by enabling retrieval in the compressed domain, satisfies this requirement. Because it contains an embedded probabilistic description of the source, this new representation allows the construction of good inference models without compromise of compression efficiency, leads to very efficient procedures for query and retrieval, and provides a framework for higher level tasks such as the analysis and classification of video shots.
Empirical Bayesian EM-based motion segmentation

A recent trend in motion-based segmentation has been to rely on statistical procedures derived from expectation-maximization (EM) principles. EM-based approaches have various advantages for segmentation, such as proceeding by taking non-greedy soft decisions regarding the assignment of pixels to regions, or allowing the use of sophisticated priors capable of imposing spatial coherence on the segmentation. A practical difficulty with such priors is, however the determination of appropriate values for their parameters. The authors exploit the fact that the EM framework is itself suited for empirical Bayesian data analysis to develop an algorithm that finds the estimates of the prior parameters which best explain the observed data. Such an approach maintains the Bayesian appeal of incorporating prior beliefs, but requires only a qualitative description of the prior avoiding the requirement of a quantitative specification of its parameters. This eliminates the need for trial-and-error strategies for parameter determination and leads to better segmentation with fewer iterations.
Frame-free video

Current digital video representations emphasize compression efficiency, lacking some of the flexibility required for interactive manipulation of digital bitstreams. We present a video representation which can encompass both space and time, providing a temporally coherent description of video sequences. The video sequence is segmented into its component objects, and the trajectory of each object throughout the sequence is described parametrically, according to a spatiotemporal motion model. Since the motion model is a continuous function of time, the video representation becomes frame-rate independent and the temporal resolution a user-definable parameter. i.e. the traditional sequence of frames, with the temporal structure hardcoded into the bitstream at the time of production, is replaced by a collection of scene snapshots assembled on the fly by the decoder. This enables random access and temporal scalability, the major building blocks for interactivity.
Spatiotemporal model-based optic flow estimation

We introduce a spatiotemporal model-based algorithm capable of providing estimates of optic flow which are coherent along a set of video frames. The algorithm is based on a spatiotemporal motion model that consists of a quadratic constraint in time and an affine constraint in space. Optic flow is computed through a delayed-decision process that incorporates knowledge about both image correlation along time, and the goodness of fit to the underlying motion-model. The temporal coherence and parametric nature of the recovered optic flow can facilitate interactive access to the video stream and improve the efficiency of tasks such as video compression, interpolation or classification.
Psychological Implications for Submarine Display Design

This paper addresses a number of psychological issues pertaining to display design. We review the literature comparing 3-D and 2-D displays and evaluate the findings in terms of sub-surface environments. In addition to the specific problem of display dimensionality this paper outlines a number of perceptual, cognitive and ecological factors that are relevant to display design for submarine environments. The Generative Transformational approach to visual perception is outlined and the relevance of transformational theory to display design is discussed. The paper also discusses a number of practical and theoretical factors relevant to empirical assessment of display utility and outlines three key areas for future research-representing uncertainty, using a cognitive model of human decision making, and conveying affective information-that have the potential to uncover novel theoretical developments and new technologies.
Individual Differences in Problem Solving and Intelligence

Studies of human problem solving have traditionally used deterministic tasks that require the execution of a systematic series of steps to reach a rational and optimal solution. Most real-world problems, however, are characterised by uncertainty, the need to consider an enormous number of variables and possible courses of action at each stage in solving the problem, and the need to optimise the solution subject to multiple interacting constraints. There are reliable individual differences in people’s abilities to solve such realistic problems. It also seems likely that people’s ability to solve these difficult problems reflects, or depends on, their intelligence. We report on a study of N = 101 adults who completed a series of visual optimisation problems (Travelling Salesperson, Minimum Spanning Tree, and Generalised Steiner Tree problems), as well as a cognitive optimisation problem (a version of the Secretary problem). We also characterised these individuals along three relevant and important cognitive abilities dimensions, fluid ability, visuo-spatial ability, and cognitive processing speed. Modelling of covariance structures indicated that performance on both types of optimisation problems relies on general intelligence and raises the possibility that they can be used to assess intelligence.
An application of minimum description length clustering to partitioning learning curves

We apply a minimum description length-based clustering technique to the problem of partitioning a set of learning curves. The goal is to partition experimental data collected from different sources into groups of sources that are statistically the same. We solve this problem by defining statistical models for the data generating processes, then partitioning them using the normalized maximum likelihood criterion. Unlike many alternative model selection methods, this approach is optimal (in a minimax coding sense) for data of any sample size. We present an application of the method to the cognitive modeling problem of partitioning of human learning curves for different categorization tasks
Bayesian statistical inference in psychology: comment on Trafimow (2003).

D. Trafimow (2003) presented an analysis of null hypothesis significance testing (NHST) using Bayes's theorem. Among other points, he concluded that NHST is logically invalid, but that logically valid Bayesian analyses are often not possible. The latter conclusion reflects a fundamental misunderstanding of the nature of Bayesian inference. This view needs correction, because Bayesian methods have an important role to play in many psychological problems where standard techniques are inadequate. This comment, with the help of a simple example, explains the usefulness of Bayesian inference for psychology.
Modeling individual differences in cognition

Many evaluations of cognitive models rely on data that have been averaged or aggregated across all experimental subjects, and so fail to consider the possibility of important individual differences between subjects. Other evaluations are done at the single-subject level, and so fail to benefit from the reduction of noise that data averaging or aggregation potentially provides. To overcome these weaknesses, we have developed a general approach to modeling individual differences using families of cognitive models in which different groups of subjects are identified as having different psychological behavior. Separate models with separate parameterizations are applied to each group of subjects, and Bayesian model selection is used to determine the appropriate number of groups. We evaluate this individual differences approach in a simulation study and show that it is superior in terms of the key modeling goals of prediction and understanding. We also provide two practical demonstrations of the approach, one using the ALCOVE model of category learning with data from four previously analyzed category learning experiments, the other using multidimensional scaling representational models with previously analyzed similarity data for colors. In both demonstrations, meaningful individual differences are found and the psychological models are able to account for this variation through interpretable differences in parameterization. The results highlight the potential of extending cognitive models to consider individual differences.
2 Minimum Description Length and Psychological Clustering Models

Clustering is one of the most basic and useful methods of data analysis. This chapter describes a number of powerful clustering models, developed in psychology, for representing objects using data that measure the similarities between pairs of objects. These models place few restrictions on how objects are assigned to clusters, and allow for very general measures of the similarities between objects and clusters. Geometric Complexity Criteria (GCC) are derived for these models, and are used to fit the models to similarity data in a way that balances goodness-of-fit with complexity. Complexity analyses, based on the GCC, are presented for the two most widely used psychological clustering models, known as " additive clustering " and " additive trees " .
Evidence accumulation in decision making: Unifying the “take the best” and the “rational” models

An evidence accumulation model of forced-choice decision making is proposed to unify the fast and frugaltake the best (TTB) model and the alternativerational (RAT) model with which it is usually contrasted. The basic idea is to treat the TTB model as a sequential-sampling process that terminates as soon as any evidence in favor of a decision is found and the rational approach as a sequential-sampling process that terminates only when all available information has been assessed. The unified TTB and RAT models were tested in an experiment in which participants learned to make correct judgments for a set of real-world stimuli on the basis of feedback, and were then asked to make additional judgments without feedback for cases in which the TTB and the rational models made different predictions. The results show that, in both experiments, there was strong intraparticipant consistency in the use of either the TTB or the rational model but large interparticipant differences in which model was used. The unified model is shown to be able to capture the differences in decision making across participants in an interpretable way and is preferred by the minimum description length model selection criterion.
A Simple Method for Generating Additive Clustering Models with Limited Complexity

Additive clustering was originally developed within cognitive psychology to enable the development of featural models of human mental representation. The representational flexibility of additive clustering, however, suggests its more general application to modeling complicated relationships between objects in non-psychological domains of interest. This paper describes, demonstrates, and evaluates a simple method for learning additive clustering models, based on the combinatorial optimization approach known as Population-Based Incremental Learning. The performance of this new method is shown to be comparable with previously developed methods over a set of ‘benchmark’ data sets. In addition, the method developed here has the potential, by using a Bayesian analysis of model complexity that relies on an estimate of data precision, to determine the appropriate number of clusters to include in a model.
Pupil Size and Mental Load

This experiment explores whether the size of human pupil dilation is a measure that can discriminate between easier and harder intellectual tasks undertaken by the participant when information is displayed on a computer monitor. While it has been previously found that a participant’s pupil dilation will be larger during harder intellectual tasks, these experiments have not generally been conducted under the environmental condition of light radiated from a computer monitor. The findings of this experiment indicate that computer monitor’s radiance did not interfere with the researcher’s ability to discriminate successfully between the participant’s taskrelated pupil dilation.
Models, Parameters and Priors in Bayesian Inference

Batchelder and Smith (2004) critiqued the use of Bayesian statistical inference for model selection and evaluation, arguing that it can lead to invalid and contradictory conclusions. Their argument centered around a series of concrete examples purporting to show the problems inherent in adopting the Bayesian approach. This paper addresses Batchelder and Smith’s critique by re-analyzing their examples using correct Bayesian methods, and demonstrates that their negative conclusions are not warranted. Throughout the re-analysis, the central role of information in the Bayesian approach is emphasized. In particular, it is argued a careful consideration of available information is required to understand the relationship between parameters, models, and data, and the setting of prior probability distributions.
Modeling Individual Differences in Category Learning

Many evaluations of cognitive models rely on data that have been averaged or aggregated across all experimental subjects, and so fail to consider the possibility that there are important individual differences between subjects. Other evaluations are done at the single-subject level, and so fail to benefit from the reduction of noise that data averaging or aggregation potentially provides. To overcome these weaknesses, we develop a general approach to modeling individual differences using families of cognitive models, where different groups of subjects are identified as having different psychological behavior. Separate models with separate parameterizations are applied to each group of subjects, and Bayesian model selection is used to determine the appropriate number of groups. We demonstrate the general approach in a concrete and detailed way using the ALCOVE model of category learning and data from four previously analysed category learning experiments. Meaningful individual differences are found for three of the four experiments, and ALCOVE is able to account for this variation through psychologically interpretable differences in parameterization. The results highlight the potential of extending cognitive models to consider individual differences.
Common and distinctive features in stimulus similarity: A modified version of the contrast model

Featural representations of similarity data assume that people represent stimuli in terms of a set of discrete properties. In this article, we consider the differences in featural representations that arise from making four different assumptions about how similarity is measured. Three of these similarity models— the common features model, the distinctive features model, and Tversky’s seminal contrast model—have been considered previously. The other model is new and modifies the contrast model by assuming that each individual feature only ever acts as a common or distinctive feature. Each of the four models is tested on previously examined similarity data, relating to kinship terms, and on a new data set, relating to faces. In fitting the models, we have used the geometric complexity criterion to balance the competing demands of data-fit and model complexity. The results show that both common and distinctive features are important for stimulus representation, and we argue that the modified contrast model combines these two components in a more effective and interpretable way than Tversky’s original formulation.
Sequential sampling models of human text classification

Text classification involves deciding whether or not a document is about a given topic. It is an important problem in machine learning, because automated text classifiers have enormous potential for application in information retrieval systems. It is also an interesting problem for cognitive science, because it involves real world human decision making with complicated stimuli. This paper develops two models of human text document classification based on random walk and accumulator sequential sampling processes. The models are evaluated using data from an experiment where participants classify text documents presented one word at a time under task instructions that emphasize either speed or accuracy, and rate their confidence in their decisions. Fitting the random walk and accumulator models to these data shows that the accumulator provides a better account of the decisions made, and a “balance of evidence” measure provides the best account of confidence. Both models are also evaluated in the applied information retrieval context, by comparing their performance to established machine learning techniques on the standard Reuters-21578 corpus. It is found that they are almost as accurate as the benchmarks, and make decisions much more quickly because they only need to examine a small proportion of the words in the document. In addition, the ability of the accumulator model to produce useful confidence measures is shown to have application in prioritizing the results of classification decisions.
The Perception of Minimal Structures: Performance on Open and Closed Versions of Visually Presented Euclidean Travelling Salesperson Problems

The planar Euclidean version of the travelling salesperson problem (TSP) requires finding a tour of minimal length through a two-dimensional set of nodes. Despite the computational intractability of the TSP, people can produce rapid, near-optimal solutions to visually presented versions of such problems. To explain this, MacGregor et al (1999, Perception 28 1417–1428) have suggested that people use a global-to-local process, based on a perceptual tendency to organise stimuli into convex figures. We review the evidence for this idea and propose an alternative, local-to-global hypothesis, based on the detection of least distances between the nodes in an array. We present the results of an experiment in which we examined the relationships between three objective measures and performance measures of optimality and response uncertainty in tasks requiring participants to construct a closed tour or an open path. The data are not well accounted for by a process based on the convex hull. In contrast, results are generally consistent with a locally focused process based initially on the detection of nearest-neighbour clusters. Individual differences are interpreted in terms of a hierarchical process of constructing solutions, and the findings are related to a more general analysis of the role of nearest neighbours in the perception of structure and motion.