Data Compression: The Complete Reference
Data Compression: The Complete Reference
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Spatio-temporal features for robust content-based video copy detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Learning to Localize Objects with Structured Output Regression
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Efficient Visual Search of Videos Cast as Text Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Query expansion for hash-based image object retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Global Kernel Density Mode Seeking: Applications to Localization and Tracking
IEEE Transactions on Image Processing
An integrated approach for content-based video object segmentation and retrieval
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Circuits and Systems for Video Technology
Robust segmentation and tracking of colored objects in video
IEEE Transactions on Circuits and Systems for Video Technology
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Traditional methods for the content-based object retrieval suffers high space and time consumption. In this paper, we propose a novel method for the efficient object retrieval in videos. First, we compress the feature descriptors by tracking the feature points between consecutive frames and encoding the tracked feature points. The encoding procedures are performed by applying the motion prediction in video codec. Second, we propose a new algorithm to locate the objects by searching for the high-density positions of the related feature points in frames. To improve the speed, we count the corresponding words of feature points within the query target and calculate their spatial distributions. Experimental results show that the proposed method outperforms the previous methods.