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According to the study of biological vision, human visual attention is aroused by observer's tasks and it is a top-down control mechanism. A top-down visual attention model based on similarity distance is proposed in this study. Top-down visual attention model is realized by using similarity distance to obtain visual expectation based on improved Itti's model. A task map is selected by using improved...
We present a general method for integrating visual components into a multi-modal cognitive system. The integration is very generic and can work with an arbitrary set of modalities. We illustrate our integration approach with a specific instantiation of the architecture schema that focuses on integration of vision and language: a cognitive system able to collaborate with a human, learn and display...
This work discusses the application of an Artificial Intelligence technique called data extraction and a process-based ontology in constructing experimental qualitative models for video retrieval and detection. We present a framework architecture that uses multimodality features as the knowledge representation scheme to model the behaviors of a number of human actions in the video scenes. The main...
Salient regions extraction plays an important role in image analysis and processing, and it is widely used in image compression, coding, content-based image retrieve and so on. The use of salient regions can improve the efficiency of image processing and reduce the computational complexity. An approach for salient regions extraction in natural image is proposed in this paper. By analyzing the phase...
Emotion modeling evoked by natural scenes is challenging issue. In this paper, we propose a novel scheme for analyzing the emotion reflected by a natural scene, considering the human emotional status. Based on the concept of original GIST, we developed the fuzzy-GIST to build the emotional feature space. According to the relationship between emotional factors and the characters of image, L*C*H* color...
We present a novel bottom-up saliency detection algorithm. Our method computes so-called local regression kernels (i.e., local features) from the given image, which measure the likeness of a pixel to its surroundings. Visual saliency is then computed using the said ldquoself-resemblancerdquo measure. The framework results in a saliency map where each pixel indicates the statistical likelihood of saliency...
Detection of saliency regions in images is useful for object based image understanding and object localization. In our work, we investigate a saliency region detection algorithm based on the human visual attention (HVA) model. In the first phase, we use mutual information and probability-of-boundary (PoB) for color saliency and edge detection respectively to filter SURF (speeded up robust features)...
Visual attention region determination simulates the behavior of the human visual system and determines visual attention regions in an image. In this study, a visual attention region determination approach using low-level features, including luminance, color, and region information, is proposed. First, the contrast map is attained by computing the contrast between each pixel and its ldquothresholdingrdquo...
Considering the gap between low-level image features and the high-level semantic concept in content-based image retrieval (CBIR), a new approach is proposed for image retrieval based on visual saliency, by analyzing the human visual perception process. Visual information is introduced as the new feature which reflects high-level semantic concept objectively. First, the visual saliency model for image...
Visual information presentations on small displays are being increased as the use of mobile communication is increasing day by day. Digital image is one of the most popular forms of visual information which is easily shared and accessible. However, a challenge is to provide a better user experience on heterogeneous small display sizes. In this paper, a novel detail and efficient algorithm is proposed...
A biologically motivated salient regions detection approach is proposed to identify the salient regions within complex natural images. Multi-scale image features such as intensity, color and orientation are extracted to get some feature maps. The log spectra of the feature maps are analyzed in frequency spectrum domain and the spectra residual are extracted. Then the corresponding feature saliency...
This research proposes a model of the multidimensional metadata generation approach for detecting human action in video. The idea is to develop a multidimensional multimodal framework, which will use a semantic approach on the action recognition and classification level. The main idea of the model is the inputs/outputs in the model will be the results of recognition processes from different modalities...
This paper presents a method which able to integrate audio and visual information for action scene analysis in any movie. The approach is top-down for determining and extract action scenes in video by analyzing both audio and video data. In this paper, we directly modelled the hierarchy and shared structures of human behaviours, and we present a framework of the hidden Markov model based application...
Artificial emotion study will be of utmost importance in future artificial intelligence research. In this paper, an emotion understanding system based on brain activity and ldquoGISTrdquo is newly proposed to categorize emotions reflected by natural scenes. According to the strong relationship of human emotion and the brain activity, functional magnetic resonance imaging (fMRI) and electroencephalography...
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