Conventional block-based classification is based on the labeling of individual blocks of an image, disregarding any adjacency information. When analyzing a small region of an image, it is sometimes difficult even for a person to tell what the image is about. Hence, the drawback of context-free use of visual features is recognized up front. This paper studies a context-dependant classifier based on a two dimensional Hidden Markov Model. In particular we explore how the balance between structural information and content description affect the precision in a semantic feature extraction scenario. We train a set of semantic classes using the development video archive annotated by the TRECVid 2005 participants. To extract semantic features the classes with maximum a posteriori probability are searched jointly for all blocks. Preliminary results indicate that the performance of the system can be increased by varying the block size.
The amount of digitized video in archives is becoming so huge, that easier access and content browsing tools are desperately needed. Also, video is no longer one big piece of data, but a collection of useful smaller building blocks, which can be accessed and used independently from the original context of presentation. In this paper, we demonstrate a content model for audio video sequences, with the purpose of enabling the automatic generation of video summaries. The model is based on descriptors, which indicate various properties and relations of audio and video segments. In practice, these descriptors could either be generated automatically by methods of analysis, or produced manually (or computer-assisted) by the content provider. We analyze the requirements and characteristics of the different data segments, with respect to the problem of summarization, and we define our model as a set of constraints, which allow to produce good quality summaries.
Conference Committee Involvement (5)
Multimedia Content Analysis, Management, and Retrieval 2006
18 January 2006 | San Jose, California, United States
Multimedia Systems and Applications VIII
24 October 2005 | Boston, MA, United States
Storage and Retrieval Methods and Applications for Multimedia 2005
18 January 2005 | San Jose, California, United States
Storage and Retrieval Methods and Applications for Multimedia 2004
20 January 2004 | San Jose, California, United States