1,723,637 research outputs found
Human perception of image complexity: real scenes versus texture patches
No full textThe aim of this work is to study image complexity perception of real images. We conducted psycho-physical experiments where observers judged the complexity of different datasets of images on a web-based interface [1]. At the end of the test, observers indicated the main characteristics that guided their judgements. The databases differed in the type of visual stimuli used: images representing real scenes and/or texture patches. For real scenes the most relevant criteria used were quantity of objects, details and colors, while for texture patches they were regularity and understandability. Several criteria are adopted simultaneously, confirming the multidimensional aspect of complexity found in the literature [2]. To process the subjective data we applied z-scores and outlier removal. The mean scores are then correlated with different visual features. We considered features based on spatial, color and frequency properties that can be associated to bottom-up processes. To take into account top-down effects like understandability we included a memorability index [3]. We propose an image complexity measure where the features are linearly combined. The optimal weighting coefficients are those that best fit the subjective data and depend on the type of stimuli considered. Our measure, properly tuned, can predict complexity perception of different kind of images, outperforming the single visual features. From our investigation two aspects of image complexity can be underlined: many different perceptual properties are involved and their relative influence depends on the type of stimuli. These considerations are supported by both our computational proposal and the verbal description analysis.
[1] Ciocca G, Corchs S, Gasparini F, Bricolo E, Tebano R. Does color influence image complexity perception? In: Fifth IAPR Computational Color Imaging Workshop vol. 9016 of Lecture Notes in Computer Science. Springer Berlin/Heidelberg; ((2015) ):139–148
[2] Oliva A, Mack ML, Shrestha M. Identifying the Perceptual Dimensions of Visual Complexity of Scenes. In: Proc. 26th Annual Meeting of the Cognitive Science Society ((2004) ):101–106
[3] Isola P, Xiao J, Torralba A, and Oliva A. What makes an image memorable? In IEEE Conference on Computer Vision and Pattern Recognition ((2011) ):145–15
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Ensemble learning on visual and textual data for social image emotion classification
No full textTexts, images and other information are posted everyday on the social network and provides a large amount of multimodal data. The aim of this work is to investigate if combining and integrating both visual and textual data permits to identify emotions elicited by an image. We focus on image emotion classification within eight emotion categories: amusement, awe, contentment, excitement, anger, disgust, fear and sadness. Within this classification task we here propose to adopt ensemble learning approaches based on the Bayesian model averaging method, that combine five state-of-the-art classifiers. The proposed ensemble approaches consider predictions given by several classification models, based on visual and textual data, through respectively a late and an early fusion schemes. Our investigations show that an ensemble method based on a late fusion of unimodal classifiers permits to achieve high classification performance within all of the eight emotion classes. The improvement is higher when deep image representations are adopted as visual features, compared with hand-crafted ones
Affective audio analysis using objective features
No full textSeveral studies exist in the literature that address the problem of emotion classification of visual stimuli but less effort has been devoted to emotion classification of audio stimuli. The most of these studies start from the analysis of physiological signals such as EEG data [1]. The aim of this work is to evaluate if it is possible to classify audio signals according to elicited emotions using only objective features. In our analysis we adopt the IADS (International Affective Digitized Sound) database [2], composed of 167 auditory stimuli. The database provides pleasure, arousal and dominance ratings for each audio stimulus, recorded from 100 subjects during psycho physical test. The database is formed by different type of audio: from environmental sounds to music, as well as from single sound to complex ones. We start considering the affective dimension of valence within the three categorical classes of low, medium and high pleasure. To investigate this classification task we consider 35 features both in time and frequency domain. With these features, we test three types of classifiers: Bayesian, K Nearest Neighbor and Classification and Regression Tree [3]. We apply a feature selection strategy in order to find the more significant features. Using these features and the Bayesian classifier we have reached an average accuracy of 45%. A similar result is achieved using physiological signals [1]. Starting from our results we believe that dividing each audio files in frames and applying a windowing strategy to evaluate objective features, the final classification performance could significantly increase
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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