Video Semantic Concept Research Articles

Incorporating feature hierarchy and boosting to achieve more effective classifier training and concept-oriented video summarization and skimming

For online medical education purposes, we have developed a novel scheme to incorporate the results of semantic video classification to select the most representative video shots for generating concept-oriented summarization and skimming of surgery education videos . First, salient objects are used as the video patterns for feature extraction to achieve a good representation of the intermediate video semantics. The salient objects are defined as the salient video compounds that can be used to characterize the most significant perceptual properties of the corresponding real world physical objects in a video, and thus the appearances of such salient objects can be used to predict the appearances of the relevant semantic video concepts in a specific video domain. Second, a novel multi-modal boosting algorithm is developed to achieve more reliable video classifier training by incorporating feature hierarchy and boosting to dramatically reduce both the training cost and the size of training samples, thus it can significantly speed up SVM (support vector machine) classifier training. In addition, the unlabeled samples are integrated to reduce the human efforts on labeling large amount of training samples. Finally, the results of semantic video classification are incorporated to enable concept-oriented video summarization and skimming. Experimental results in a specific domain of surgery education videos are provided.

MODELING SEMANTIC CONCEPTS AND USER PREFERENCES IN CONTENT-BASED VIDEO RETRIEVAL

In this paper, a user-centered framework is proposed for video database modeling and retrieval to provide appealing multimedia experiences on the content-based video queries. By incorporating the Hierarchical Markov Model Mediator (HMMM) mechanism, the source videos, segmented video shots, visual/audio features, semantic events, and high-level user perceptions are seamlessly integrated in a video database. With the hierarchical and stochastic design for video databases and semantic concept modeling, the proposed framework supports the retrieval for not only single events but also temporal sequences with multiple events. Additionally, an innovative method is proposed to capture the individual user's preferences by considering both the low-level features and the semantic concepts. The retrieval and ranking of video events and the temporal patterns can be updated dynamically online to satisfy individual user's interest and information requirements. Moreover, the users' feedbacks are efficiently accumulated for the offline system training process such that the overall retrieval performance can be enhanced periodically and continuously. For the evaluation of the proposed approach, a soccer video retrieval system is developed, presented, and tested to demonstrate the overall retrieval performance improvement achieved by modeling and capturing the user preferences.

Concept-Oriented Indexing of Video Databases: Toward Semantic Sensitive Retrieval and Browsing

Digital video now plays an important role in medical education, health care, telemedicine and other medical applications. Several content-based video retrieval (CBVR) systems have been proposed in the past, but they still suffer from the following challenging problems: semantic gap, semantic video concept modeling, semantic video classification, and concept-oriented video database indexing and access. In this paper, we propose a novel framework to make some advances toward the final goal to solve these problems. Specifically, the framework includes: 1) a semantic-sensitive video content representation framework by using principal video shots to enhance the quality of features; 2) semantic video concept interpretation by using flexible mixture model to bridge the semantic gap; 3) a novel semantic video-classifier training framework by integrating feature selection, parameter estimation, and model selection seamlessly in a single algorithm; and 4) a concept-oriented video database organization technique through a certain domain-dependent concept hierarchy to enable semantic-sensitive video retrieval and browsing.

On-line knowledge- and rule-based video classification system for video indexing and dissemination

Current information and communication technologies provide the infrastructure to transport bits anywhere, but do not indicate how to easily and precisely access and/or route information at the semantic level. To facilitate intelligent access to the rich multimedia data over the Internet, we develop an on-line knowledge- and rule-based video classification system that supports automatic “indexing” and “filtering” based on the semantic concept hierarchy. This paper investigates the use of video and audio content analysis, feature extraction and clustering techniques for further video semantic concept classification. A supervised rule-based video classification system is proposed using video automatic segmentation, annotation and summarization techniques for seamless information browsing and updating. In the proposed system, a real-time scene-change detection proxy performs an initial video-structuring process by splitting a video clip into scenes. Motional, visual and audio features are extracted in real-time for every detected scene by using on-line feature-extraction proxies. Higher semantics are then derived through a joint use of low-level features along with classification rules in the knowledge base. Classification rules are derived through a supervised learning process that relies on some representative samples from each semantic category. An indexing and filtering process can now be built using the semantic concept hierarchy to personalize multimedia data based on users’ interests. In real-time filtering, multiple video streams are blocked, combined, or sent to certain channels depending on whether or not the video streams are matched with the user's profile. We have extensively experimented and evaluated the classification and filtering techniques using basketball sports video data. In particular, in our experiment, the basketball video structure is examined and categorized into different classes according to distinct motional, visual and audio characteristics features by a rule-based classifier. The concept hierarchy describing the motional/visual/audio feature descriptors and their statistical relationships are reported in this paper along with detailed experimental results using on-line sports videos.

Factor graph framework for semantic video indexing

Video query by semantic keywords is one of the most challenging research issues in video data management. To go beyond low-level similarity and access video data content by semantics, we need to bridge the gap between the low-level representation and high-level semantics. This is a difficult multimedia understanding problem. We formulate this problem as a probabilistic pattern-recognition problem for modeling semantics in terms of concepts and context. 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 appear in context. To model this interaction explicitly, we propose a network of multijects (multinet). To model the multinet computationally, we propose a factor graph framework which can enforce spatio-temporal constraints. Using probabilistic models for multijects, rocks, sky, snow, water-body, and forestry/greenery, and using a factor graph as the multinet, we demonstrate the application of this framework to semantic video indexing. We demonstrate how detection performance can be significantly improved using the multinet to take inter-conceptual relationships into account. Our experiments using a large video database consisting of clips from several movies and based on a set of five semantic concepts reveal a significant improvement in detection performance by over 22%. We also show how the multinet is extended to take temporal correlation into account. By constructing a dynamic multinet, we show that the detection performance is further enhanced by as much as 12%. With this framework, we show how keyword-based query and semantic filtering is possible for a predetermined set of concepts.

A probabilistic framework for semantic video indexing, filtering, and retrieval

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.