Graph-based multi-space semantic correlation propagation for video retrieval

Feng, Bailan; Cao, Juan; Bao, Xiuguo; Bao, Lei; Zhang, Yongdong; Lin, Sheng; Yun, Xiaochun

doi:10.1007/s00371-010-0510-6

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…The image retrieval part was achieved through user-input keywords and user-selected object shapes. A concept-based video retrieval approach, named graph-based multi-space semantic correlation propagation [17], made use of a manifold-ranking algorithm to explore the user query and concepts in a concept graph. We have implemented a system called M-ReFind [18], which allows users to manually annotate current access context (i.e., access time, location and concurrent activities) for media files or web pages of interest.…”

Section: Visual Computing Methodsmentioning

confidence: 99%

ShoppingCat: relocating products from the Web in online shopping

Zhao

Deng

Feng

2013

Sci. China Inf. Sci.

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A trust-aware random walk model for return propensity estimation and consumer anomaly scoring in online shopping SCIENCE CHINA Information Sciences 62, 052101 (2019); CRISPy-web: An online resource to design sgRNAs for CRISPR applications Synthetic and Systems Biotechnology 1, 118 (2016); Web search and data mining of natural products and their bioactivities in PubChem SCIENCE CHINA Chemistry 56, 1424 (2013); Online Web news extraction via tag path feature weighted by text block density SCIENTIA SINICA Informationis 47, 1078 (2017);. RESEARCH PAPER .

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Section: Visual Computing Methodsmentioning

confidence: 99%

ShoppingCat: relocating products from the Web in online shopping

Zhao

Deng

Feng

2013

Sci. China Inf. Sci.

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“…However, since this strategy does not consider relative differences between regions (e.g., their appearance and positional differences), it is not very robust. Inspired by the all-pairs strategy of edit propagation [54], [55], [56], [57], we define the similarity between any two regions I i and I j using a Gaussian of the color difference,…”

Section: Global Image Complexitymentioning

confidence: 99%

Change Blindness Images

Wong

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Change blindness refers to human inability to recognize large visual changes between images. In this paper, we present the first computational model of change blindness to quantify the degree of blindness between an image pair. It comprises a novel context-dependent saliency model and a measure of change, the former dependent on the site of the change, and the latter describing the amount of change. This saliency model in particular addresses the influence of background complexity, which plays an important role in the phenomenon of change blindness. Using the proposed computational model, we are able to synthesize changed images with desired degrees of blindness. User studies and comparisons to state-of-the-art saliency models demonstrate the effectiveness of our model.

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“… The raw lecture videos from the internet database have a long and random video files, and hence the retrieval model has to be made more efficient with the features of content-based indexing, browsing, video location and searching [4].  The major difference gap between the visual features and the query of the user allows the video retrieval complicated [13]. The semantic gap should be improved for the better performance.…”

Section: Challengesmentioning

confidence: 99%

Semantic Enriched Lecture Video Retrieval System Using Feature Mixture and Hybrid Classification

Daga¹,

Thakare

2017

AIVP

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The advancement in the web technologies has increased the lecture video contents tremendously. The lecture video retrieval for the e-learning process is a challenging task since the videos are unstructured and have a large size. Since many video lectures have less information, the video retrieval system needs to be built with the enhanced features to improve the efficiency of the retrieval process. In this paper, a semantic enriched lecture video retrieval system has been proposed. The key frames from the video are extracted through the pre-processing. The proposed model uses the feature mixture database with the more relevant features such as text, semantic word, and the Local Gabor Pattern (LGP) vectors. The video retrieval from the feature mixture database is done by using the hybrid K-Nearest Neighbour Naive Bayes (KNB) classifier. This classifier uses the techniques of both the Naive Bayes (NB) classifier and the K-Nearest Neighbour (K-NN) classifier. The performance metrics such as precision, recall and the f-measure analyze the efficiency of the proposed model. Simulation is done by giving the text query and the video query to the video database. The simulation results show that the proposed model has better precision and recall value of 1.0 and 0.7500 respectively. The f-measure of the proposed model has a better value of 0.8519 than the existing K-NN system.

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Graph-based multi-space semantic correlation propagation for video retrieval

Cited by 11 publications

References 27 publications

ShoppingCat: relocating products from the Web in online shopping

ShoppingCat: relocating products from the Web in online shopping

Change Blindness Images

Semantic Enriched Lecture Video Retrieval System Using Feature Mixture and Hybrid Classification

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