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ObjectBook construction for large-scale semantic-aware image retrieval

ObjectBook construction for large-scale semantic-aware image...

引用

3rd IEEE International Workshop on Multimedia Signal processing, MMSP 2011

作者： Zhang, Shiliang Tian, Qi Huang, Qingming Gao, Wen Key Lab. of Intelli. Info. Process. Inst. of Comput. Tech. CAS Beijing 100190 China Department of Computer Science University of Texas San Antonio TX78249 United States Graduate University Chinese Academy of Sciences Beijing 100049 China Peking University No. 5 Yiheyuan Road Beijing 100871 China

ISBN: (纸本)9781457714337

Automatic image annotation assigns semantic lab.ls to images thus presents great potential to achieve semantic-aware image retrieval. However, existing annotation algorithms are not scalab.e to this emerging need, both in terms of computational efficiency and the number of tags they can deal with. Facilitated by recent development of the large-scale image category recognition data such as ImageNet, we extrapolate from it a model for scalab.e image annotation and semantic-aware image retrieval, namely ObjectBook. The element in the ObjectBook, which is called an ObjectWord, is defined as a collection of discriminative image patches annotated with the corresponding objects. We take ObjectBook as a high-level semantic preserving visual vocabulary, and hence are able to easily develop efficient image annotation and inverted file indexing strategies for large-scale image collections. The proposed retrieval strategy is compared with state-of-the-art algorithms. Experimental results manifest that the ObjectBook is both discriminative and scalab.e for large-scale semantic-aware image retrieval. © 2011 IEEE.

关键词： Image retrieval

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Human group activity analysis with fusion of motion and appearance info.mation 11

Human group activity analysis with fusion of motion and appe...

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19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11

作者： Cheng, Zhongwei Qin, Lei Huang, Qingming Jiang, Shuqiang Yan, Shuicheng Tian, Qi Graduate University of Chinese Academy of Sciences Beijing 100190 China Key Lab of Intelli. Info. Process. Inst. of Comput. Tech CAS Beijing 100190 China National University of Singapore Electrical and Computer Engineering Singapore 117576 Singapore Department of Computer Science University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9781450306164

Human activity analysis is an important and challenging task in video content analysis and understanding. In this paper, we focus on the activity of small human group, which involves countable persons and complex interactions. To cope with the variant number of participants and inherent interactions within the activity, we propose a hierarchical model with three layers to depict the characteristics at different granularities. In traditional methods, group activity is represented mainly based on motion info.mation, such as human trajectories, but ignoring discriminative appearance info.mation, e.g. the rough sketch of a pose style. In our approach, we take advantage of both the motion and the appearance info.mation in the spatiotemporal activity context under the hierarchical model. These features are inhomogeneous. Therefore, we employ multiple kernel learning methods to fuse the features for group activity recognition. Experiments on a surveillance-like human group activity database demonstrate the validity of our approach and the recognition performance is promising. Copyright 2011 ACM.

关键词： Hierarchical systems

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Music video affective understanding using feature importance analysis 10

Music video affective understanding using feature importance...

引用

ACM International Conference on Image and Video Retrieval, ACM-CIVR 2010

作者： Cui, Yue Jin, Jesse S. Zhang, Shiliang Luo, Suhuai Tian, Qi University of Newcastle Callaghan 2308 NSW Australia Key Lab. of Intelli. Info. Process. Inst. of Comput. Tech. CAS Beijing 100190 China University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9781450301176

Music video is a popular type of entertainment by viewers. Currently, the novel indexing and retrieval approach based on the affective cues contained in music videos becomes more and more attractive to users. Music video affective analysis and understanding is one of the most popular topics in current multimedia community. In this paper, we propose a novel feature importance analysis approach to select most representative arousal and valence features for arousal and valence modeling. Compared with state-of-the-art work by Zhang on music video affective analysis, our main contributions are in the following aspects: (1) Another 3 affect-related features are extracted to enrich the feature set and exploit their correlation with arousal and valence. (2) All extracted features are ordered via feature importance analysis, and then optimal feature subset is selected after ordering. (3) Different regression methods are compared for arousal and valence modeling in order to find the fittest estimation function. Our method achieves 33.39% and 42.17% deduction in terms of mean absolute error compared with Zhang's method. Experimental results demonstrate our proposed method has a considerable improvement on music video affective understanding. Copyright © 2010 ACM.

关键词： Regression analysis

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Fast rate-distortion optimized transform for Intra coding

Fast rate-distortion optimized transform for Intra coding

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28th Picture Coding Symposium, PCS 2010

作者： Zhao, Xin Zhang, Li Ma, Siwei Gao, Wen Key Lab. of Intelli. Info. Process. Inst. of Comput. Tech. CAS Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China Institute of Digital Media Peking University Beijing 100871 China

ISBN: (纸本)9781424471348

In our previous work, the rate-distortion optimized transform (RDOT) is introduced for Intra coding, which is featured by the usage of multiple offline-trained transform matrix candidates. The proposed RDOT achieves remarkable coding gain for KTA Intra coding, while maintaining almost the same computational complexity at the decoder. However, at the encoder, the computational complexity is increased drastically by the expensive rate-distortion (R-D) optimized selection of transform matrix. To resolve this problem, in this paper, we propose a fast RDOT scheme using macroblock- and block-level R-D cost thresholding. With the proposed method, unnecessary mode trials and R-D evaluations of transform matrices can be efficiently skipped from the mode decision process. Extensive experimental results show that, with negligible coding performance degradation, about 88.9% of the total encoding time is saved by the proposed method. © 2010 IEEE.

关键词： Computational complexity

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Group activity recognition by gaussian processes estimation

Group activity recognition by gaussian processes estimation

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International Conference on Pattern Recognition

作者： Cheng, Zhongwei Qin, Lei Huang, Qingming Jiang, Shuqiang Tian, Qi Graduate University of Chinese Academy of Sciences Beijing 100190 China Key Lab. of Intelli. Info. Process Inst. of Comput. Tech. CAS Beijing 100190 China Department of Computer Science University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9780769541099

Human action recognition has been well studied recently, but recognizing the activities of more than three persons remains a challenging task. In this paper, we propose a motion trajectory based method to classify human group activities. Gaussian processes are introduced to represent human motion trajectories from a probabilistic perspective to handle the variability of people's activities in group. With respect to the relationships of persons in group activities, three discriminative descriptors are designed, which are Individual, Dual and Unitized Group Activity Pattern. We adopt the Bag of Words approach to solve the problem of unbalanced number of persons in different activities. Experiments are conducted on the human group-activity video database, and the results show that our approach outperforms the state-of-the-art. © 2010 IEEE.

关键词： Gaussian distribution

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Multiple kernel learning with high order kernels

Multiple kernel learning with high order kernels

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2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Wang, Shuhui Jiang, Shuqiang Huang, Qingming Tian, Qi Key Lab. of Intelli. Info. Process Inst. of Comput. Tech. CAS Beijing 100190 China Graduate School Chinese Academy of Sciences 100190 Beijing China Department of Computer Science University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9780769541099

Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements have been achieved over methods using single kernel, the advantages of employing multiple kernels for machine learning are far from being fully developed. In this paper, we propose to use "high order kernels" to enhance the learning of MKL when a set of original kernels are given. High order kernels are generated by the products of real power of the original kernels. We incorporate the original kernels and high order kernels into a unified localized kernel logistic regression model. To avoid over-fitting, we apply group LASSO regularization to the kernel coefficients of each training sample. Experiments on image classification prove that our approach outperforms many of the existing MKL approaches. © 2010 IEEE.

关键词： Image classification

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Action recognition using spatial-temporal context

Action recognition using spatial-temporal context

引用

International Conference on Pattern Recognition

作者： Hu, Qiong Qin, Lei Huang, Qingming Jiang, Shuqiang Tian, Qi Key Lab. of Intelli. Info. Process. Inst. of Comput. Tech. CAS Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100190 China Department of Computer Science University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9780769541099

The spatial-temporal local features and the bag of words representation have been widely used in the action recognition field. However, this framework usually neglects the internal spatial-temporal relations between video-words, resulting in ambiguity in action recognition task, especially for videos "in the wild". In this paper, we solve this problem by utilizing the volumetric context around a video-word. Here, a local histogram of video-words distribution is calculated, which is referred as the "context" and further clustered into contextual words. To effectively use the contextual info.mation, the descriptive video-phrases (ST-DVPs) and the descriptive video-cliques (ST-DVCs) are proposed. A general framework for ST-DVP and ST-DVC generation is described, and then action recognition can be done based on all these representations and their combinations. The proposed method is evaluated on two challenging human action datasets: the KTH dataset and the YouTube dataset. Experiment results confirm the validity of our approach. © 2010 IEEE.

关键词： Pattern recognition

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Building contextual visual vocabulary for large-scale image applications

Building contextual visual vocabulary for large-scale image ...

引用

18th ACM International Conference on Multimedia ACM Multimedia 2010, MM'10

作者： Zhang, Shiliang Huang, Qingming Hua, Gang Jiang, Shuqiang Gao, Wen Tian, Qi Key Lab. of Intelli. Info. Process. Inst. of Comput. Tech. CAS Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100049 China IBM Research T. J. Watson Center NY 10523 United States Peking University No. 5 Yiheyuan Road Beijing 100871 China Computer Science Depart. University of Texas San Antonio TX 78249 United States

ISBN: (纸本)9781605589336

Not withstanding its great success and wide adoption in Bag-of-visual Words representation, visual vocabulary created from single image local features is often shown to be ineffective largely due to three reasons. First, many detected local features are not stable enough, resulting in many noisy and non-descriptive visual words in images. Second, single visual word discards the rich spatial contextual info.mation among the local features, which has been proven to be valuable for visual matching. Third, the distance metric commonly used for generating visual vocabulary does not take the semantic context into consideration, which renders them to be prone to noise. To address these three confrontations, we propose an effective visual vocabulary generation framework containing three novel contributions: 1) we propose an effective unsupervised local feature refinement strategy;2) we consider local features in groups to model their spatial contexts;3) we further learn a discriminant distance metric between local feature groups, which we call discriminant group distance. This group distance is further leveraged to induce visual vocabulary from groups of local features. We name it contextual visual vocabulary, which captures both the spatial and semantic contexts. We evaluate the proposed local feature refinement strategy and the contextual visual vocabulary in two large-scale image applications: large-scale near-duplicate image retrieval on a dataset containing 1.5 million images and image search re-ranking tasks. Our experimental results show that the contextual visual vocabulary shows significant improvement over the classic visual vocabulary. Moreover, it outperforms the state-of-the-art Bundled Feature in the terms of retrieval precision, memory consumption and efficiency. © 2010 ACM.

关键词： Semantics

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Building pair-wise visual word tree for efficent image re-ranking

Building pair-wise visual word tree for efficent image re-ra...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shiliang Zhang Qingming Huang Yijuan Lu Gao Wen Qi Tian Key Lab of Intelli. Info. Process Inst. of Comput. Tech. CAS Beijing China Chinese Academy and Sciences Beijing China Graduate University of Chinese Academy of Sciences Beijing China Department of Computer Science Texas State University San Marcos TX USA Department of Computer Science University of Texas at San Antonio TX USA

Bag-of-visual Words (BoW) image representation is getting popular in computer vision and multimedia communities. However, experiments show that the traditional BoW representation is not as effective as it is desired. One of the most important reasons for its ineffectiveness is that, the traditional BoW representation lost the spatial info.mation in images. To overcome this problem, we propose the pair-wise visual word tree, within which each visual word keeps both the appearance and spatial info.mation between two interest points in image. Thus, the corresponding novel BoW representation preserves the spatial structure in image. Based on the pair-wise visual word tree, we propose an efficient topic word selection algorithm, which utilizes the Latent Semantic Analysis to discover the most expressive visual words for different image categories. An efficient strategy is then utilized to combine the selected topic words for image re-ranking. Massive experiments show that the novel BoW representation shows promising performance. Meanwhile, the proposed image re-ranking strategy shows the state-of-the-art precision and promising efficiency.

关键词： Quantization Object recognition Computer science Image analysis Content addressable storage Image representation Computer vision Algorithm design and analysis Image processing Event detection

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Descriptive visual words and visual phrases for image applications 09

Descriptive visual words and visual phrases for image applic...

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17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Zhang, Shiliang Tian, Qi Hua, Gang Huang, Qingming Li, Shipeng Key Lab. of Intelli. Info. Process. Inst. of Comput. Tech. CAS Beijing 100080 China Microsoft Research Asia Beijing 100080 China Microsoft Live Labs. Research Redmond WA 78052 United States Graduate University of Chinese Academy of Sciences Beijing 100049 China

ISBN: (纸本)9781605586083

The Bag-of-visual Words (BoW) image representation has been applied for various problems in the fields of multimedia and computer vision. The basic idea is to represent images as visual documents composed of repeatable and distinctive visual elements, which are comparable to the words in texts. However, massive experiments show that the commonly used visual words are not as expressive as the text words, which is not desirable because it hinders their effectiveness in various applications. In this paper, Descriptive Visual Words (DVWs) and Descriptive Visual Phrases (DVPs) are proposed as the visual correspondences to text words and phrases, where visual phrases refer to the frequently co-occurring visual word pairs. Since images are the carriers of visual objects and scenes, novel descriptive visual element set can be composed by the visual words and their combinations which are effective in representing certain visual objects or scenes. Based on this idea, a general framework is proposed for generating DVWs and DVPs from classic visual words for various applications. In a large-scale image database containing 1506 object and scene categories, the visual words and visual word pairs descriptive to certain scenes or objects are identified as the DVWs and DVPs. Experiments show that the DVWs and DVPs are compact and descriptive, thus are more comparable with the text words than the classic visual words. We apply the identified DVWs and DVPs in several applications including image retrieval, image re-ranking, and object recognition. The DVW and DVP combination outperforms the classic visual words by 19.5% and 80% in image retrieval and object recognition tasks, respectively. The DVW and DVP based image re-ranking algorithm: DWPRank outperforms the state-of-the-art VisualRank by 12.4% in accuracy and about 11 times faster in efficiency. Copyright 2009 ACM.

关键词： Object recognition

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