Taking the bite out of automated naming of characters in TV video

We investigate the problem of automatically labelling appearances of characters in TV or film material with their names. This is tremendously challenging due to the huge variation in imaged appearance of each character and the weakness and ambiguity of available annotation. However, we demonstrate that high precision can be achieved by combining multiple sources of information, both visual and textual. The principal novelties that we introduce are: (i) automatic generation of time stamped character annotation by aligning subtitles and transcripts; (ii) strengthening the supervisory information by identifying when characters are speaking. In addition, we incorporate complementary cues of face matching and clothing matching to propose common annotations for face tracks, and consider choices of classifier which can potentially correct errors made in the automatic extraction of training data from the weak textual annotation. Results are presented on episodes of the TV series ‘‘Buffy the Vampire Slayer”

Profiling a set of personality traits of text author: what our words reveal about us

Authorship profiling, i.e. revealing information about an unknown author by analyzing their text, is a task of growing importance. One of the most urgent problems of authorship profiling (AP) is selecting text parameters which may correlate to an author’s personality. Most researchers’ selection of these is not underpinned by any theory. This article proposes an approach to AP which applies neuroscience data. The aim of the study is to assess the probability of self-destructive behaviour of an individual via formal parameters of their texts. Here we have used the “Personality Corpus”, which consists of Russian-language texts. A set of correlations between scores on the Freiburg Personality Inventory scales that are known to be indicative of self-destructive behaviour (“Spontaneous Aggressiveness”, “Depressiveness”, “Emotional Lability”, and “Composedness”) and text variables (average sentence length, lexical diversity etc.) has been calculated. Further, a mathematical model which predicts the probability of self-destructive behaviour has been obtained

Overview of the 2005 cross-language image retrieval track (ImageCLEF)

The purpose of this paper is to outline efforts from the 2005 CLEF crosslanguage image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content-based retrieval methods for cross-language image retrieval. Four tasks were offered in the ImageCLEF track: a ad-hoc retrieval from an historic photographic collection, ad-hoc retrieval from a medical collection, an automatic image annotation task, and a user-centered (interactive) evaluation task that is explained in the iCLEF summary. 24 research groups from a variety of backgrounds and nationalities (14 countries) participated in ImageCLEF. In this paper we describe the ImageCLEF tasks, submissions from participating groups and summarise the main fndings