Existing video indexing and retrieval methods on popular web-based multimedia sharing websites are based on user-provided sparse tagging. This paper proposes a very specific way of searching for video clips, based on the content of the video. We present our work on Content-based Video Indexing and Retrieval using the Correspondence-Latent Dirichlet Allocation (corr-LDA) probabilistic framework. This is a model that provides for auto-annotation of videos in a database with textual descriptors, and brings the added benefit of utilizing the semantic relations between the content of the video and text. We use the concept-level matching provided by corr-LDA to build correspondences between text and multimedia, with the objective of retrieving content with increased accuracy. In our experiments, we employ only the audio components of the individual recordings and compare our results with an SVM-based approach.
In recent years research has focused on the use of internal features of images and videos computed in an automated or semi-automated way @cite_1 . Automated analysis calculates statistics, which can be approximately correlated to the content features. This is useful as it provides information without costly human interaction.
@cite_1
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We propose an original approach for the characterization of video dynamic content with a view to supplying new functionalities for motion-based video indexing and retrieval with query by example. We have designed a statistical framework for motion content description without any prior motion segmentation, and for motion-based video classification and retrieval. Contrary to other proposed methods, we do not extract from a given video sequence a set of motion features but we identify a global probabilistic model, expressed as a temporal Gibbs random field. This leads to define a efficient statistical motion-based similarity measure, relying on the computation of conditional likelihoods, to discriminate various motion contents. We have carried out experiments on a set of 100 video sequences, representative of various motion situations (temporal textures as fire and crowd motions, sport videos, car sequences, low motion activity examples). We have obtained promising results both for the video classification step and for the retrieval process.