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Together with the technology advancement, Computer Vision plays an important role in enhancing smart computing systems to help people overcome obstacles in their daily lives. One of the common troublesome problems is human memorization ability, especially memorizing things such as personal items. It is annoying for people to waste their time finding lost items manually by recall or notes. This motivates...
Selective attention in the human visual system is performed as the way that humans focus on the most important parts when observing a visual scene. Many bottom-up computational models of visual attention have been devised to get the saliency map for an image, which are data-driven or task-independent. However, studies show that the task-driven or top-down mechanism also plays an important role during...
Mass of information intelligence services are a hot research field of current information, As the massive network of information, particularly audio and video information has a huge amount of data, non-structural, high dimension, semantic feature of diversity, putting forward a new challenge in the mass of information in data integration, deep mining and intelligence analysis. Traditional information...
We propose a statistical framework for high-level feature extraction that uses SIFT Gaussian mixture models (GMMs) and audio models. SIFT features were extracted from all the image frames and modeled by a GMM. In addition, we used mel-frequency cepstral coefficients and ergodic hidden Markov models to detect high-level features in audio streams. The best result obtained by using SIFT GMMs in terms...
Many content-based image mining systems extract local features from images to obtain an image description based on discrete feature occurrences. Such applications require a visual vocabulary also known as visual codebook or visual dictionary to discretize the extracted high-dimensional features to visual words in an efficient yet accurate way. Once such an application operates on images of a very...
It is important basis for virtual plant model to dynamically simulate the development process of plant on computer. The features can be extracted from the experimental results, then the plant growth information and environmental information used to be integrated together. According to the topological rule of plant and morphological change of organs, the morphological models of plant could be built...
Salient region extraction provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. In this paper, we propose a robust approach to extracting the salient region based on bottom-up visual attention. The main contributions are twofold: 1) Instead of the feature parallel integration, the proposed saliencies are derived by serial...
Story boundary detection is the foundation of content based news video retrieval. In this paper, Naive Bayes Model, which has been successfully used in multi-modal feature fusion, is implemented in news video story segmentation. Firstly, we get candidate boundaries through shot detection. Secondly, middle-level features such as visual features, audio type, motion and caption, are extracted from shots...
This paper presents extensive experiments on sport event images using the bag-of-words model. We propose a simple but effective combination of feature extraction and visual dictionary formation to boost the performance of naive Bayes classifier based on BOW model. Despite of not being a novel idea, our algorithm offers encouraging performance in event recognition domain. Moreover, in certain degrees,...
In this paper we proposed a novel feature fusion technique in Saliency-Based Visual Attention Model, presented in [Itti, 1998]. There are three conspicuity maps in Saliency-Based Visual Attention Model, which are linearly combined from 12 color maps, 6 intensity maps and 24 orientation maps (42 Feature maps overall) through an Across-scale combination and normalization. We utilized the genetic algorithm...
The detection of region of interest (ROI) in medical images has played a very important role in computer aided diagnose. With respect to liver-focus pixels having weak textural and similar intensities with their neighborhood, a novel detection algorithm of abnormal regions in liver CT images has been proposed in this paper by visual attention model. Firstly, a set of statistical texture features for...
This work studies a new approach for image retrieval on largescale community databases. Our proposed system explores two different modalities: visual features and community-generated metadata, such as tags. We use topic models to derive a high-level representation appropriate for retrieval for each of our images in the database. We evaluate the proposed approach experimentally in a query-by-example...
Latent semantic indexing (LSI), as a popular textual information retrieval approach, has been used heavily for many years. However, the use of the approach in image retrieval has been limited. In this paper, a method of using LSI in combination with the salient image representation based on a saliency-based bottom-up visual attention computational model (VACM) motivated by visual physiological experimental...
Modeling visual attention provides an alternative methodology to image description in many applications such as adaptive content delivery and image retrieval. In this paper, we propose a robust approach to the modeling bottom-up visual attention. The main contributions are twofold: 1) a novel contextual texture feature is extracted to describe texture consistency of a region globally. And then the...
Combining bottom-up and top-down attention influences, a novel region extraction model which based on object-accumulated visual attention mechanism is proposed in this paper. Compared with early research, the new approach brings in prior information at the proper time, updates scan path dynamically, needs less computational resources and reduces the probability to direct the attention to a less-meaning...
In this paper, we propose an approach to automatic detection of semantic object. The method provides an effective content expression pattern for semantic analysis and retrieval of video. In the moving semantic object detection model, motion contrast is computed based on the planar motion (homography) between frames, which is estimated by applying RANSAC algorithm on point correspondences in the scene...
We propose a new effective shape descriptor, chord context, for shape description in content-based image retrieval. For a shape, the chord context describes a frequency distribution of chord lengths with different orientations. The histograms which represent the chord context are compacted and normalized into a feature matrix. Unlike other shape representation schemes, the proposed scheme is able...
The virtual product data model in its different stages is an essential prerequisite for the virtual modeling of processes and their virtual simulation. Product design starts with conceptual design including the concepts for the future productpsilas shape and function - the gestalt. In this stage, the designers work on fuzzy product data interpreted in so-called scribbles and applied to perception...
Computing technology is radically changing the manner in which we work and communicate with computers. ubiquitous virtual reality (U-VR) has been researched in order to apply the concept of virtual reality and its technology into ubiquitous computing. In this paper, we analyze past research on ubiquitous virtual reality and find future research direction.
The saliency map model proposed by Itti and Koch has been a popular method in explaining the guidance of visual attention using only bottom-up information. The method makes one-level salient-point extraction, and does not take human visual resolution into account. We propose a hierarchical architecture to identify salient regions in a multiple-layer manner. Two ways of attention movements are introduced...
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