Photobook: content-based manipulation of image databases
International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
On an algorithm of Zemlyachenko for subtree isomorphism
Information Processing Letters
An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
On programming of arithmetic operations
Communications of the ACM
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Content Matching of MPEG-4 Video Objects
PCM '01 Proceedings of the Second IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Object of interest-based visual navigation, retrieval, and semantic content identification system
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Object Level Grouping for Video Shots
International Journal of Computer Vision
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Retrieval of objects in video by similarity based on graph matching
Pattern Recognition Letters
Content-based object organization for efficient image retrieval in image databases
Decision Support Systems
| Hi-index | 0.00 |
In this paper, we tackle the problem of matching of objects in video in the framework of the rough indexing paradigm. In this context, the video data are of very low spatial and temporal resolution because they come from partially decoded MPEG compressed streams. This paradigm enables us to achieve our purpose in near real time due to the faster computation on rough data than on original full spatial and temporal resolution video frames. In this context, segmentation of rough video frames is inaccurate and the region features (texture, color, shape) are not strongly relevant. The structure of the objects must be considered in order to improve the robustness of the matching of regions. The problem of object matching can be expressed in terms of region adjacency graph (RAG) matching. Here, we propose a directed acyclic graph (DAG) matching method based on a heuristic in order to approximate object matching. The RAGs to compare are first transformed into DAGs by orienting edges. Then, we compute some combinatoric metrics on nodes in order to classify them by similarity. At the end, a top-down process on DAGs aims to match similar patterns that exist between the two DAGs. The results are compared with those of a method based on relaxation matching.