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Access to reliable scientific knowledge is crucial to making informed decisions for both policymakers and citizens. However, scientific documents are inherently difficult due to their complex terminology and vernacular. Automatic text ...

• Analysis of the assessment of full-page Handwritten Text Recognition (HTR) systems.

The evaluation of Handwritten Text Recognition (HTR) systems has traditionally used metrics based on the edit distance between HTR and ground truth (GT) transcripts, at both the character and word levels. This is very adequate when the ...

The purpose of this study is to present an exhaustive analysis on research paper recommender systems which have become very popular and gained a lot of research attention. Though the major focus is on developing new recommendation ...

Multi-label classification (MLC) has recently attracted increasing interest in the machine learning community. Several studies provide surveys of methods and datasets for MLC, and a few provide empirical comparisons of MLC methods. ...

• A comprehensive empirical investigation of multi-label classification methods.
• ...

Object tracking is a very interesting problem in computer vision. Numerous algorithms have been developed to solve object tracking problems for several decades. Among various techniques, in this article, we review most of the existing traditional ...

Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve ...

The diminishing return property of ERR (Expected Reciprocal Rank) is highly intuitive and attractive: its user model says, for example, that after the users have found a highly relevant document at rank r, few of them will continue to examine rank ...$\mathrm{}$

Quantification is the task of estimating, given a set $\sigma$ of unlabelled items and a set of classes $\mathcal{C}=\{c_1,\dots ,c_{|\mathcal{C}|}\}$, the prevalence (or “relative frequency”) in $\sigma$ of each class $c_i\in \mathcal{C}$. While quantification may in principle be solved by classifying each ...$$${}_{}$

Objective and effective algorithm performance evaluation results are an important basis for the selection of tracking algorithms. Problems in the existing performance evaluation of moving target tracking algorithms include an enlarge number of ...

Dense motion estimations obtained from optical flow techniques play a significant role in many image processing and computer vision tasks. Remarkable progress has been made in both theory and its application in practice. In this paper, we provide ...

• Introducing optical flow: the basic concepts, the characteristics of the variational and CNN-based techniques, and the evaluation measures.
• Discussing developments of the variational method, analyzing the challenges and illustrating ...

• Evaluation measures have been used arbitrarily in multilabel classification experiments, without an objective analysis of correlation or bias.

In many important application domains, such as text categorization, scene classification, biomolecular analysis and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label ...

Nowadays, most streaming data sources are becoming high dimensional. Accordingly, subspace stream clustering, which aims at finding evolving clusters within subgroups of dimensions, has gained a significant importance. However, in spite of the rich ...

The approach presented in this paper applies fuzzy set theory to the evaluation of audio signals segmentation with high resolution and accuracy. The method is based on comparing automatically found boundaries with ground truth. Hence, the method is more ...

The evaluation of clustering results is one of the most important issues in cluster analysis, a core task for effective information access. There are two types of measures for evaluating the quality of clustering results: internal and external. External ...

Hierarchical classification addresses the problem of classifying items into a hierarchy of classes. An important issue in hierarchical classification is the evaluation of different classification algorithms, an issue which is complicated by the ...

This paper presents a new adaptive binarization method for the degraded document images. Variable background, non-uniform illumination, and blur caused by humidity are the addressed degradations. The proposed method has four steps: contrast analysis, ...

This paper presents the design and implementation of a posture classification method. A new feature extraction strategy according to curvelet transform is provided for identifying the posture in images. First of all, human body is segmented. For ...

Many real-world networks have a structure of overlapping cohesive groups. In order to uncover this structure several clustering algorithms have been developed. In this paper, we focus on the evaluation of these algorithms. Quality measures are commonly ...

In recent years, the need for greater accountability and improvement in teaching's quality has become a major issue in higher education. The results of student evaluation of teaching are increasingly used to make judgments about teaching quality, career ...

The adaptive integration of the colour and texture attributes in the development of complex image descriptors is one of the most investigated topics of research in computer vision. The substantial interest shown by the research community in colour-...