Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Using the Inner-Distance for Classification of Articulated Shapes
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Shape Recognition and Retrieval Using String of Symbols
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
International Journal of Computer Vision
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Shape recognition and retrieval: A structural approach using velocity function
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
EMD-L1: an efficient and robust algorithm for comparing histogram-based descriptors
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Robust contour matching via the order-preserving assignment problem
IEEE Transactions on Image Processing
Video copy detection by fast sequence matching
Proceedings of the ACM International Conference on Image and Video Retrieval
A feature sequence kernel for video concept classification
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Sequence-based kernels for online concept detection in video
AIEMPro '11 Proceedings of the 2011 ACM international workshop on Automated media analysis and production for novel TV services
Information Processing and Management: an International Journal
Sequence kernels for clustering and visualizing near duplicate video segments
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Efficient graffiti image retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Human action recognition and retrieval using sole depth information
Proceedings of the 20th ACM international conference on Multimedia
Actions speak louder than words: searching human action video based on body movement
Proceedings of the 20th ACM international conference on Multimedia
Similarity retrieval of angiogram images BASED on a flexible shape model
FIMH'13 Proceedings of the 7th international conference on Functional Imaging and Modeling of the Heart
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We present an approach to measuring similarities between visual data based on approximate string matching. In this approach, an image is represented by an ordered list of feature descriptors. We show the extraction of local features sequences from two types of 2-D signals - scene and shape images. The similarity of these two images is then measured by 1) solving a correspondence problem between two ordered sets of features and 2) calculating similarities between matched features and dissimilarities between unmatched features. Our experimental study shows that such a globally ordered and locally unordered representation is more discriminative than a bag-of-features representation and the similarity measure based on string matching is effective. We illustrate the application of the proposed approach to scene classification and shape retrieval, and demonstrate superior performance to existing solutions.