CSMCCVA:Framework of cross-modal semantic mapping based on cognitive computing of visual and auditory sensations

Cross-modal semantic mapping and cross-media retrieval are key problems of the multimedia search engine.This study analyzes the hierarchy,the functionality,and the structure in the visual and auditory sensations of cognitive system,and establishes a brain-like cross-modal semantic mapping framework based on cognitive computing of visual and auditory sensations.The mechanism of visual-auditory multisensory integration,selective attention in thalamo-cortical,emotional control in limbic system and the memory-enhancing in hippocampal were considered in the framework.Then,the algorithms of cross-modal semantic mapping were given.Experimental results show that the framework can be effectively applied to the cross-modal semantic mapping,and also provides an important significance for brain-like computing of non-von Neumann structure.

AN OBJECT ORIENTED MODEL SCHEDULING FOR MEDIA-SOC

This paper proposes an object oriented model scheduling for parallel computing in media MultiProcessors System on Chip(MPSoC).Firstly,the Coarse Grain Data Flow Graph(CGDFG) parallel programming model is used in this approach.Secondly,this approach has the feature of unified abstraction for software objects implementing in processor and hardware objects implementing in ASICs,easy for mapping CGDFG programming on MPSoC.This approach cuts down the kernel overhead and reduces the code size effectively.The principle of the oriented object model,the method of scheduling,and how to map a parallel programming through CGDFG to the MPSoC are analyzed in this approach.This approach also compares the code size and execution cycles with conventional control flow scheduling,and presents respective management overhead for one application in me-dia-SoC.

Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

This paper proposes a new algorithm based on low-rank matrix recovery to remove salt &pepper noise from surveillance video. Unlike single image denoising techniques, noise removal from video sequences aims to utilize both temporal and spatial information. By grouping neighboring frames based on similarities of the whole images in the temporal domain, we formulate the problem of removing salt &pepper noise from a video tracking sequence as a lowrank matrix recovery problem. The resulting nuclear norm and L1-norm related minimization problems can be efficiently solved by many recently developed methods. To determine the low-rank matrix, we use an averaging method based on other similar images. Our method can not only remove noise but also preserve edges and details. The performance of our proposed approach compares favorably to that of existing algorithms and gives better PSNR and SSIM results.

Synchronization-Oriented Placement and Retrieval Strategies for Delay-Sensitive Media Streams

Multimedia data include text, image, audio and video, etc. Recent deve-lopments and advances in the areas of mass storage technology and high speed networks make it feasible for multimedia computing systems to offer services such as multimedia e-mail, teleconferencing and various vivid games. The stor-age and retrieval of multimedia data are two of the most important technologies in the future multimedia systems. This paper analyzes the synchronization re-quirements of the delay-sensitive media streams, classifies the synchronization hierarchically and proposes several effective strategies for the intra-media and inter-media data placement problems.