Spatiotemporal Saliency Detection: State of Art

Saliency detection has become a very prominent subject for research in recent time. Many techniques has been defined for the saliency detection.In this paper number of techniques has been explained that include the saliency detection from the year 2000 to 2015, almost every technique has been included.all the methods are explained briefly including their advantages and disadvantages. Comparison between various techniques has been done. With the help of table which includes authors name,paper name,year,techniques,algorithms and challenges. A comparison between levels of acceptance rates and accuracy levels are made

A multimodal approach for the automatic assessment of viewer subjective perception of Youtube videos

This research project is focused on the understanding of how saliency could influence the impression perceived by a viewer of a video. However, this perception cannot be perfectly assessed since some inherent bias is inevitable. This problem is an existing error that should be understood and taken into account in any statistical analysis or experiments. For instance, and focused on this research, the perception of a viewer can be affected by tendencies, inclinations or feelings of the individual (Attentional bias). In order to lessen the impact of this issue it is important to define a simplified research field, limiting tendencies or feelings of the viewers. Due this, car commercials were chosen as the video domain for the study. These and other following choices were previously stablished by Alejandro Hernández García in his project called Aesthetics Assessment of Videos through Visual Descriptors and Automatic Polarity Annotation which will be named several times in this research. This car commercial database was extracted from YouTube and was simplified obtaining finally a collection of 138 car commercial videos. This collection will be used for this project too and will be the domain for the proposed computational model. This way, we will demonstrate the impact of the saliency in subjective perception, focusing this assumption on car commercial videos extracted from YouTube. As consequence of this assumption, saliency also would modify the video ratings in this platformIngeniería de Sistemas Audiovisuale

Spatiotemporal saliency for video classification

Computer vision applications often need to process only a representative part of the visual input rather than the whole image/sequence. Considerable research has been carried out into salient region detection methods based either on models emulating human visual attention (VA) mechanisms or on computational approximations. Most of the proposed methods are bottom-up and their major goal is to filter out redundant visual information. In this paper, we propose and elaborate on a saliency detection model that treats a video sequence as a spatiotemporal volume and generates a local saliency measure for each visual unit (voxel). This computation involves an optimization process incorporating inter- and intra-feature competition at the voxel level. Perceptual decomposition of the input, spatiotemporal center-surround interactions and the integration of heterogeneous feature conspicuity values are described and an experimental framework for video classification is set up. This framework consists of a series of experiments that shows the effect of saliency in classification performance and let us draw conclusions on how well the detected salient regions represent the visual input. A comparison is attempted that shows the potential of the proposed method