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.
The common strategy for automatic indexing had been based on using syntactic features alone. However, due to its complexity of operation, there has been a paradigm shift in the research concerned with identifying semantic features @cite_3 . User-friendly Content-Based Retrieval (CBR) systems operating at semantic level would identify motion-features as the key besides other features like color, objects etc., because motion (either of camera motion or shot editing) adds to the meaning of the content. The focus of present motion based systems had been mainly in identifying the principal object and performing retrieval-based on cues derived from such motion. With the objective of deriving semantic-level indices, it becomes important to deal with the learning tools. The learning phases followed by the classification phase are two common envisioned steps in CBR systems. Rather than the user mapping the features with semantic categories, the task could be shifted to the system to perform learning (or training) with pre-classified samples and determine the patterns in an effective manner. A concise review of these techniques is provided in @cite_9 @cite_0 .
@cite_0 @cite_9 @cite_3
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Video indexing and retrieval have a wide spectrum of promising applications, motivating the interest of researchers worldwide. This paper offers a tutorial and an overview of the landscape of general strategies in visual content-based video indexing and retrieval, focusing on methods for video structure analysis, including shot boundary detection, key frame extraction and scene segmentation, extraction of features including static key frame features, object features and motion features, video data mining, video annotation, video retrieval including query interfaces, similarity measure and relevance feedback, and video browsing. Finally, we analyze future research directions. This study surveys current trends methods in video retrieval. The major themes covered by the study include shot segmentation, key frame extraction, feature extraction, clustering, indexing and video retrieval-by similarity, probabilistic, transformational, refinement and relevance feedback. This work has done in an aim to assist the upcoming researchers in the field of video retrieval, to know about the techniques and methods available for video retrieval.