Abstract
A novel boundary detection scheme based on 'edge flow' is proposed in this paper. This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By propagating the edge flow vectors, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme facilitates integration of color and texture into a single framework for boundary detection. Segmentation results on a large and diverse collections of natural images are provided, demonstrating the usefulness of this method to content based image retrieval.
| Original language | English |
|---|---|
| Pages (from-to) | 1375-1388 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 9 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - Aug 2000 |
| Externally published | Yes |
Bibliographical note
Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].Funding
Manuscript received November 19, 1998; revised February 15, 2000. This work was supported in part by the National Science Foundation under Grants IRI-9411330 and IRI-9704785. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Jeffrey J. Rodriguez.