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.
Video query by semantic keywords is one of the most difficult problems in multimedia data retrieval. The difficulty lies in the mapping between low-level video representation and high-level semantics. In @cite_19 , the multimedia content-access problem is formulated as a multimedia pattern recognition problem. A probabilistic framework is proposed to map the low-level video representation to high level semantics using the concepts of and their networks called .
@cite_19
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Semantic filtering and retrieval of multimedia content is crucial for efficient use of the multimedia data repositories. Video query by semantic keywords is one of the most difficult problems in multimedia data retrieval. The difficulty lies in the mapping between low-level video representation and high-level semantics. We therefore formulate the multimedia content access problem as a multimedia pattern recognition problem. We propose a probabilistic framework for semantic video indexing, which call support filtering and retrieval and facilitate efficient content-based access. To map low-level features to high-level semantics we propose probabilistic multimedia objects (multijects). Examples of multijects in movies include explosion, mountain, beach, outdoor, music etc. Semantic concepts in videos interact and to model this interaction explicitly, we propose a network of multijects (multinet). Using probabilistic models for six site multijects, rocks, sky, snow, water-body forestry greenery and outdoor and using a Bayesian belief network as the multinet we demonstrate the application of this framework to semantic indexing. We demonstrate how detection performance can be significantly improved using the multinet to take interconceptual relationships into account. We also show how the multinet can fuse heterogeneous features to support detection based on inference and reasoning. Image and video retrieval continues to be one of the most exciting and fastest-growing research areas in the field of multimedia technology. What are the main challenges in image and video retrieval? Despite the sustained efforts in the last years, we think that the paramount challenge remains bridging the semantic gap. By this we mean that low level features are easily measured and computed, but the starting point of the retrieval process is typically the high level query from a human. Translating or converting the question posed by a human to the low level features seen by the computer illustrates the problem in bridging the semantic gap. However, the semantic gap is not merely translating high level features to low level features. The essence of a semantic query is understanding the meaning behind the query. This can involve understanding both the intellectual and emotional sides of the human, not merely the distilled logical portion of the query but also the personal preferences and emotional subtons of the query and the preferential form of the results. Content based indexing is critical to the effective access of the multimedia data. To this end, visual data is often annotated with textual content for bridging the semantic gap. In this paper, we present a method to generate frame level fine grained annotations for a given video clip. Access to the frame level fine grained annotations lead to rich, dense and meaningful semantic associations between the text and video. This in turn makes the video retrieval systems more accurate. We demonstrate the use of probabilistic label consistent sparse coding and dictionary learning with a K-SVD algorithm to generate ‘fine grained’ annotations for a class of videos - lawn tennis. The algorithm simultaneously learns a classifier and a dictionary to generate the frame level annotations for the tennis videos using available textual descriptions. The utility of the proposed algorithm is demonstrated on a publicly available tennis dataset comprising of tennis match videos from Olympics games. In this paper, we tackle the problem of retrieving videos using complex natural language queries. Towards this goal, we first parse the sentential descriptions into a semantic graph, which is then matched to visual concepts using a generalized bipartite matching algorithm. Our approach exploits object appearance, motion and spatial relations, and learns the importance of each term using structure prediction. We demonstrate the effectiveness of our approach on a new dataset designed for semantic search in the context of autonomous driving, which exhibits complex and highly dynamic scenes with many objects. We show that our approach is able to locate a major portion of the objects described in the query with high accuracy, and improve the relevance in video retrieval.