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检索条件"机构=Key Laboratory of Intell. Information Process. Institute of Comput. Technology"

共 47 条记录，以下是31-40 订阅

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Color Maximal-Dissimilarity Pattern for pedestrian detection

Color Maximal-Dissimilarity Pattern for pedestrian detection

引用

International Conference on Pattern Recognition

作者： Qingyuan Wang Junbiao Pang Guoyi Liu Lei Qin Qingming Huang Shuqiang Jiang Chinese Academy of Sciences China College of Computer Science and Technology Beijing University of Technology China NEC Laboratories China Beijing China Key Laboratory of Intell. Information Process. Institute of Comput. Technology CAS China

ISBN: (纸本)9781467322164

Feature plays an important role in pedestrian detection, and considerable progress has been made on shape-based descriptors. However, color cues have barely been devoted to detection tasks, seemingly due to the variable appearance of pedestrians. In this paper, Color Maximal-Dissimilarity Pattern (CMDP) is proposed to encode color cues by two core operations, i.e., oriented filtering and max-pooling, which emulate the functions of primary visual cortex (V1). The extensively experimental results reveal that the biologically-explainable encoding scheme increases the invariance of color cues, and outperforms the state-of-the-art color descriptor in terms of both accuracy and speed.

关键词： Image color analysis Cascading style sheets Histograms Feature extraction Visualization Shape Biology

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Multi-feature metric learning with knowledge transfer among semantics and social tagging

Multi-feature metric learning with knowledge transfer among ...

引用

Conference on comput.r Vision and Pattern Recognition (CVPR)

作者： Shuhui Wang Shuqiang Jiang Qingming Huang Qi Tian Key Laboratory of Intell. Information Process.(CAS) Institute of Comput. Technology CAS Beijing China Chinese Academy of Sciences Beijing China Department of Computer Science University of Texas at San Antonio TX USA

Previous metric learning approaches learn a unified metric for all the classes on single feature representation, thus cannot be directly transplanted to applications involving multiple features, hundreds to thousands of hierarchical structured semantics and abundant social tagging. In this paper, we propose a novel multi-task multi-feature metric learning method which models the information sharing mechanism among different learning tasks. We decompose the real world multi-class problems such as semantic categorization or automatic tagging into a set of tasks where each task corresponds to several classes with strong visual correlation. We conduct metric learning to learn a set of (hyper)category-specific metrics for all the tasks. By encouraging model sharing among tasks, more generalization power is acquired. Another advantage is the capability of simultaneous learning with semantic information and social tagging based on the multi-task learning framework, and thus they both benefit from the information provided by each other. Experiments demonstrate the advantages on applications including semantic categorization and automatic tagging compared with other popular metric learning approaches.

关键词： Measurement Kernel Semantics Tagging Training Visualization Support vector machines

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A simple and effective saliency detection approach

A simple and effective saliency detection approach

引用

International Conference on Pattern Recognition

作者： Hui Zhang Weiqiang Wang Guiping Su Lijuan Duan Chinese Academy of Sciences Beijing China Key Laboratory of Intell. Information Process. Institute of Computing Technology CAS Beijing China College of Computer Science and Technology Beijing University of Technology Beijing China

This paper presents a simple and effective method to comput. the pixel saliency with full resolution in an image. First, the proposed method creates an image representation of four color channels through the modified comput.tion on the basis of Itti et al.[5]. Then the most informative channel is automatically identified from the derived four color channels. Finally, the pixel saliency is comput.d through the simple combination of contrast feature and spatial attention function on the individual channel. The proposed method is comput.tionally very simple, but it achieves a very good performance in the comparison experiments with six other saliency detection methods. On the challenging database with 1,000 images, it outperforms six other methods in both identifying salient pixels and segmenting salient regions.

关键词： Image color analysis Visualization Humans Image segmentation Databases Biological system modeling

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Online Vicept learning for web-scale image understanding

Online Vicept learning for web-scale image understanding

引用

IEEE International Conference on Image process.ng

作者： Liang Li Shuqiang Jiang Qingming Huang Key Laboratory of Intell. Information Process. Institute of Comput. Technology Chinese Academy of Sciences China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. Chinese Academy of Sciences

Web-scale image understanding is a challenging but significant task to comprehend image contents on the internet. The de-facto standard methods based on machine learning or comput.r vision still suffer from a phenomenon of visual pol-ysemia and concept polymorphism (VPCP). To resolve the VPCP, Vicept has been proposed to characterize the membership distribution between visual appearances and semantic concepts. In this paper, we propose an online Vicept learning algorithm on the base of stochastic approximations, which can scale up to large scale datasets with millions of training samples. With the help of the Vicept, we develop an extension of the spatial pyramid matching (SPM) kernel method by generalizing the Vicept as a basic semantic description. The efficiency of our approach is validated in the experiments of web-scale semantic image search and image classification on the ImageNet dataset and Caltech-256 dataset.

关键词： Visualization Semantics Dictionaries Kernel Training Support vector machines Conferences

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Query sensitive dynamic web video thumbnail generation

Query sensitive dynamic web video thumbnail generation

引用

IEEE International Conference on Image process.ng

作者： Chunxi Liu Qingming Huang Shuqiang Jiang Chinese Academy of Sciences Beijing China Key Laboratory of Intell. Information Process. Institute of Comput. Technology CAS Beijing China

With the fast rising of the video sharing websites, the online video becomes an important media for people to share messages, interests, ideas, beliefs, etc. In this paper, we propose a novel approach to dynamically generate the web video thumbnails according to user's query. Two issues are addressed: the video content representativeness of the selected video thumbnail, and the relationship between the selected video thumbnail and the user's query. For the first issue the reinforcement based algorithm is adopted to rank the frames in each video. For the second issue the relevance model based method is employed to calculate the similarity between the video frames and the query keywords. The final video thumbnail is generated by linear fusion of the above two scores. Compared with the existing web video thumbnails, which only reflect the preference of the video owner, the thumbnails generated in our approach not only consider the video content representativeness of the frame, but also reflect the intention of the video searcher. In order to show the effectiveness of the proposed method, experiments are conducted on the videos selected from the video sharing website. Experimental results and subjective evaluations demonstrate that the proposed method is effective and can meet the user's intention requirement.

关键词： Visualization Conferences Streaming media Feature extraction Context Multimedia communication

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Learning image Vicept description via mixed-norm regularization for large scale semantic image search

Learning image Vicept description via mixed-norm regularizat...

引用

Conference on comput.r Vision and Pattern Recognition (CVPR)

作者： Liang Li Shuqiang Jiang Qingming Huang Key Laboratory of Intell. Information Process. Institute of Comput. Technology Chinese Academy and Sciences China Chinese Academy and Sciences China

The paradox of visual polysemia and concept polymorphism has been a great challenge in the large scale semantic image search. To address this problem, our paper proposes a new method to generate image Vicept representation. Vicept characterizes the membership distribution between elementary visual appearances and semantic concepts, and forms a hierarchical representation of image semantic from local to global. To obtain discriminative Vicept descriptions with structural sparsity, we adopt mixed-norm regularization in the optimization problem for learning the concept membership distribution of visual word. Furthermore, considering the structure of BOV in images, visual descriptor is encoded as a weighted sum of dictionary elements using group sparse coding, which could obtain sparse representation at the image level. The wide applications of Vicept are validated in our experiments, including large scale semantic image search, image annotation, and semantic image re-ranking.

关键词： Visualization Semantics Dictionaries Image coding Image reconstruction Encoding Optimization

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Visual perception based Lagrangian rate distortion optimization for video coding

Visual perception based Lagrangian rate distortion optimizat...

引用

IEEE International Conference on Image process.ng

作者： Xi Wang Li Su Qingming Huang Chunxi Liu Key Laboratory of Intell. Information Process. Institute of Comput. Technology Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Beijing China University of the Chinese Academy of Sciences Beijing Beijing CN

In the conventional rate distortion optimization (RDO) video coding, the measure of distortion is mainly from the perspective of signal process.ng, while dose not fully take into account the characteristics of visual perception. People have concerns about not only the information of independent pixels, but also the temporal and spatial correlations between them. For different video content, human visual perception has different sensitivity. In this paper, in order to establish a RDO model which is more consistent with the human visual perception, we introduce the structural similarity and the content saliency information into the distortion metric. An adaptive Lagrange multiplier selection scheme is presented to allocate the bit resources more rationally by keeping the balance of the bit-rate and the visual quality. Experimental results show that the proposed method averagely reduces 10.14% bit-rate under the similar visual quality.

关键词： Visualization Humans Indexes Optimization Rate-distortion Decision support systems Measurement

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News video story sentiment classification and ranking

News video story sentiment classification and ranking

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Chunxi Liu Li Su Qingming Huang Shuqiang Jiang Chinese Academy and Sciences Beijing China Key Laboratory of Intell. Information Process. Institute of Computer Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781612843483

In this paper, we present a novel approach for news video story sentiment analysis. Two research challenges are addressed: news video story sentiment classification and ranking. For classification, a graph based semi-supervised learning approach is utilized to classify the news stories into sentiment classes. Graph based semi-supervised learning is able to tackle the problem of lacking labeled data. After classification, two sentiment classes are obtained: positive and negative. In order to project the news videos into sentiment space, a multimodal approach by fusing the text sentiment and visual representation scores is adopted to rank the videos in each class. For sentiment representation, inter and intra sentiment class analysis is conducted based on affinity propagation clustering and PageRank algorithm. A user study is conducted to evaluate the video ranking performance. The experimental results on the selected topics are promising and demonstrate the proposed approach is effective.

关键词： Visualization Speech recognition Joining process.s Clustering algorithms Error analysis Training data Training

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Fast and Effectively Identify Pornographic Images

Fast and Effectively Identify Pornographic Images

引用

International Conference on comput.tional intell.gence and Security

作者： Yanjun Fu Weiqiang Wang School of Information Science and Engineering Chinese Academy of Sciences Beijing China Key Laboratory of Intell. Information Process. Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9781457720086

In this paper, we present a practical solution to identifying pornographic images based on multiple low-level image features and support vector machine (SVM). First, the region of interest (ROI) is obtained from an original image based on the detection of skin-like pixels in YCbCr color space. The ROI is then classified into being acceptable or unacceptable by its size. Images without ROIs or acceptable ROIs are deemed to be benign images. For images with acceptable ROI, the color, texture and shape features are further extracted on ROI, and then fed to a SVM classifier to perform the task of recognition. Our approach has obtained 96.05% sensitivity and 96.17% specificity on a dataset containing 8,000 pornographic images and 12,500 benign images, as well as the process.ng speed of about 0.026 seconds for a PC to determine whether a given image with an average size of 420 by 433 pixels is pornographic.

关键词： Feature extraction Image color analysis Skin Support vector machines Shape Vectors Image recognition

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Matching Content-based Saliency Regions for partial-duplicate image retrieval

Matching Content-based Saliency Regions for partial-duplicat...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Liang Li Zhipeng Wu Zheng-Jun Zha Shuqiang Jiang Qingming Huang Key Laboratory of Intell. Information Process. Institute of Computer Technology Chinese Academy and Sciences China Chinese Academy and Sciences China School of Computing National University of Singapore Singapore

In traditional partial-duplicate image retrieval, images are commonly represented using the Bag-of-Visual-Words (BOV) model built from image local features, such as SIFT. Actually, there is only a small similar portion between partial-duplicate images so that such representation on the whole image is not adequate for the partial-duplicate image retrieval task. In this paper, we propose a novel perspective to retrieval partial-duplicate images with Contented-based Saliency Region (CSR). CSRs are such sub-regions with abundant visual content and high visual attention in the image. The content of CSR is represented with the BOV model while saliency analysis is employed to ensure the high visual attention of CSR. Each CSR is regarded as an independent unit to be retrieved in the dataset. To effectively retrieve the CSRs, we design a relative saliency ordering constraint, which captures a weak saliency relative layout among interest points in the CSR. Comparison experiments with four state-of-the-art methods on the standard partial-duplicate image dataset clearly verify the effectiveness of our scheme. Further, our approach can provide a more diverse retrieval result, which facilitates the interaction of portable-device users.

关键词： Visualization Image retrieval Image color analysis Histograms Image segmentation Layout

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