Extracting information about emotions in films

Salway, Andrew; Graham, M. J.

doi:10.1145/957052.957076

Cited by 22 publications

(36 citation statements)

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“…Hotspot detection for meeting summarization and/or meeting browsing (i.e., localization of events with a high level of activity in a meeting) has recently gained interest (Neiberg et al [124], Wrede and Shriberg [211]). Salway and Graham [157], Hanjalic and Xu [74] are modeling emotion in the context of information-retrieval like applications, such as movie browsing. In general, negative emotions receive more attention from researchers than positive emotions; there simply is a greater need for applications that detect negative emotions than positive emotions.…”

Section: Data Acquisition and Annotationmentioning

confidence: 99%

How does real affect affect affect recognition in speech?

Truong¹

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Section: Data Acquisition and Annotationmentioning

confidence: 99%

How does real affect affect affect recognition in speech?

Truong¹

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“…State of the art algorithms for emotion recognition usually use the speech signal and/or the facial expression [15]. Few efforts have been done to link emotions to content-based indexing and retrieval of multimedia [16]. In [17], the concept of speech-assisted facial expression analysis and synthesis is proposed and provides useful information for expression analysis.…”

Section: Related Workmentioning

confidence: 99%

Emotion based Contextual Semantic Relevance Feedback in Multimedia Information Retrieval

Singh¹,

Tripathi²

2012

IJCA

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Every query issued by a user to find some relevant information,contains the semantic and its associated contexts, but, indentifying and conveying these semantic and context (present in the query) to MIR system is a major challenge and still needs to be tackled effectively. Thus, exploiting the plausibility of context associated with semantic concept for the purpose of enhancement in retrieval of the possible relevant information, we propose Emotion Based Contextual Semantic Relevance Feedback(ECSRF) to learn, refine, discriminate and identify the current context present in a query. We will further investigate: (1) whether multimedia attributes(audio, speech along with visual) can be purposefully used to work out a current context of user's query and will be useful inreduction of search space and retrieval time; (2) whether increasing the Affective features (spoken emotional word(s)with facial expression(s)) in identifying, discriminating emotions would increase the overall retrieval performance in terms of Precision, Recall and retrieval time;(3)whether increasing the discriminating power of classifier algorithm in query perfection would increase the search accuracy with less retrieval time.We introduce an Emotion Recognition Unit(ERU) that comprises of a customized 3D spatiotemporal Gabor filter to capture spontaneous facial expression, and emotional word recognition system (combination of phonemes and visemes) to recognize the spoken emotional words. Integration of classifier algorithms GMM, SVM and CQPSB are compared in ECSRF framework to study the effect of increasing the discriminating power of classifier on retrieval performance.Observations suggest that prediction of contextual semantic relevance is feasible, and ECSRF model can benefit from incorporating such increased affective features and classifier to increase a MIR system's retrieval efficiency and contextual perceptions.

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“…To study the granularity of labeling and the overall quality of affective labeling, we explored an additional two sets of verbal labels with coarser granularity. These consisted of 22 labels suggested by [15] and 6 labels based on word described in [16]. A similar approach was used in [17] using manual placement of 40 labels for the FEELTRACE system.…”

Section: Verbal Labeling Of the Affect Curvementioning

confidence: 99%

An Affect-Based Video Retrieval System with Open Vocabulary Querying

Chan

Jones

2011

Lecture Notes in Computer Science

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Abstract. Content-based video retrieval systems (CBVR) are creating new search and browse capabilities using metadata describing significant features of the data. An often overlooked aspect of human interpretation of multimedia data is the affective dimension. Incorporating affective information into multimedia metadata can potentially enable search using this alternative interpretation of multimedia content. Recent work has described methods to automatically assign affective labels to multimedia data using various approaches. However, the subjective and imprecise nature of affective labels makes it difficult to bridge the semantic gap between system-detected labels and user expression of information requirements in multimedia retrieval. We present a novel affect-based video retrieval system incorporating an open-vocabulary query stage based on WordNet enabling search using an unrestricted query vocabulary. The system performs automatic annotation of video data with labels of well defined affective terms. In retrieval annotated documents are ranked using the standard Okapi retrieval model based on open-vocabulary text queries. We present experimental results examining the behaviour of the system for retrieval of a collection of automatically annotated feature films of different genres. Our results indicate that affective annotation can potentially provide useful augmentation to more traditional objective content description in multimedia retrieval.

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Extracting information about emotions in films

Cited by 22 publications

References 0 publications

How does real affect affect affect recognition in speech?

How does real affect affect affect recognition in speech?

Emotion based Contextual Semantic Relevance Feedback in Multimedia Information Retrieval

An Affect-Based Video Retrieval System with Open Vocabulary Querying

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