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An ontology-based evidential framework for video indexing using high-level multimodal fusion

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Abstract

This paper deals with information retrieval and semantic indexing of multimedia documents. We propose a generic scheme combining an ontology-based evidential framework and high-level multimodal fusion, aimed at recognising semantic concepts in videos. This work is represented on two stages: First, the adaptation of evidence theory to neural network, thus giving Neural Network based on Evidence Theory (NNET). This theory presents two important information for decision-making compared to the probabilistic methods: belief degree and system ignorance. The NNET is then improved further by incorporating the relationship between descriptors and concepts, modeled by a weight vector based on entropy and perplexity. The combination of this vector with the classifiers outputs, gives us a new model called Perplexity-based Evidential Neural Network (PENN). Secondly, an ontology-based concept is introduced via the influence representation of the relations between concepts and the ontological readjustment of the confidence values. To represent this relationship, three types of information are computed: low-level visual descriptors, concept co-occurrence and semantic similarities. The final system is called Ontological-PENN. A comparison between the main similarity construction methodologies are proposed. Experimental results using the TRECVid dataset are presented to support the effectiveness of our scheme.

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Notes

  1. We can also use the weight in the feature extraction step.

  2. NIST TREC-2003 Video Retrieval Benchmark defines 133 video concepts, organized hierarchically and each video data belong to one or more concepts [35].

  3. Three image datasets are used: Corel Images, Google Images, and LabelMe.

  4. The LSCOM-lite (Large-Scale Concept Ontology for Multimedia) [36] annotations include 39 concepts, which are interim results from the effort in developing a LSCOM. The dimensions consist of program category, setting/scene/site, people, object, activity, event, and graphics. A collaborative effort among participants in the TRECVid benchmark was completed to produce the annotations. Human subjects judge the presence or absence of each concept in the video shots.

  5. The concept subsumes is the most common specific concept.

  6. The balanced error rate is the average of the errors on each class. BER is used in “Performance Prediction Challenge Workshop”.

  7. NNET: Neural Network based on Evidence Theory.

  8. PENN: Perplexity-based Evidential Neural Network.

  9. Onto-PENN: Ontological readjustment of the PENN. The results presented in the rest of paper for the Onto-PENN, are given by (26) for the semantic similarity computation.

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Acknowledgements

This research was supported by Eurecom’s industrial members: Ascom, Bouygues Telecom, Cegetel, France Telecom, Hitachi, ST Microelectronics, Motorola, Swisscom, Texas Instruments and Thales.

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  1. Département Communications Multimédia, Eurécom, 2229, route des crêtes, 06904, Sophia-Antipolis, France

    Rachid Benmokhtar & Benoit Huet

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  1. Rachid Benmokhtar
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Correspondence to Rachid Benmokhtar.

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Benmokhtar, R., Huet, B. An ontology-based evidential framework for video indexing using high-level multimodal fusion. Multimed Tools Appl 73, 663–689 (2014). https://doi.org/10.1007/s11042-011-0936-5

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