{"content":[{"text":"Issues of media journaling are similar to those found in the area of content-based \n multimedia indexing and retrieval. We may think of a user speciﬁcation of journaling \n requirements as a complex content query speciﬁcation. However, rather than performing \n the content analysis off-line on stored data, on-line analysis requires real-time processing \n of (live) networked sessions. \n The two common approaches to the problem of content-based multimedia indexing \n and retrieval has been attribute-based indexing and retrieval of multimedia content, and \n a combination of feature extraction and object recognition. In comparison to the former, \n the latter has the advantage that it does not require manual entry of keyword descriptions \n which is a very time consuming operation. On the other hand the automatic indexing and \n annotation approach is computationally complex and only works satisfactory limited to \n speciﬁc domains. \n The above reasoning suggests that a knowledge-based approach combined with open \n distributed processing technology, might be a promising approach to the challenges of me- \n dia journaling. First of all, for feature extraction/object recognition to work well, speciﬁc \n domain knowledge is needed during analysis. Secondly, a distributed solution is required \n to cope with the computational complexity of feature extraction and object recognition, \n the massive amount of data to be analyzed in real time, and the scalability of the sys- \n tem with respect to the complexity of the session to be journaled and the content to be \n detected. \n One promising approach to integrating domain knowledge (semantics of user do- \n mains) into the content-analysis process is to combine (in domain speciﬁc ways) low- \n level quantitative content querying (i.e. query by colour histogram, texture, pitch, etc.) \n into higher-level qualitative content querying. Furthermore, a probabilistic approach to \n detection and recognition of content is needed, since evidence of content extracted from \n media streams can be either missed or hallucinated (false positives) by the involved algo- \n rithms. \n Our approach to the above challenges is to build upon the methodology of dynamic \n object-oriented Bayesian networks (DOOBNs) [6] [3]. In DOOBN, uncertainty can be \n handled explicitly and domain concepts may be effectivly represented as dynamic Bayesian \n network objects. Furthermore, we believe that a framework should be continuously adapt- \n able and scalable to the available resources. Such an approach allows the system to guide \n the user in trading between the reliability and latency of the content analysis. This re- \n quires that a framework for on-line content analysis must be resource aware based on an \n open distributed resource model. \n In this paper we propose an architecture for a distributed journaling framework, and \n an approach for formulating and transforming high-level media content queries into dis- \n tributable conﬁgurations of media content analysis algorithms. The contribution of the \n architectural framework lies in the unique combination of probabilistic knowledge-based \n media content analysis with QoS and resource awareness to handle real-time require- \n ments. \n The rest of the paper is organized as follows. In Section 2 we present an overview of \n the architecture of our distributed media journaling framework. In Section 3 we describe \n the requirements of content analysis in media journaling, and show how these require- \n ments can be met by dynamic object-oriented Bayesian networks. Section 4 discusses \n architectural requirements to on-line media content analysis and suggests a corresponding \n architecture for the distributed content analysis. This section also discusses requirements \n to resource management. In Section 5 we discuss some related work, while we in Section","bbox":{"left":0.143002,"top":0.081486,"width":0.722684,"height":0.8254779999999999}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}