Name
Abstract | ||
|---|---|---|
A novel Visual-Region-Descriptor-based approach is developed in this paper to facilitate more effective Sketch-based Image Retrieval (SBIR), which can be treated as a problem of bilateral visual mapping and modeled as an inter-related correlation distribution over visual semantic representations of sketches and images. For crossing the matching barrier between binary query sketches and full color natural images, we focus on constructing a visual pre-analysis via the sketch-like representation transformation to improve the general sketch-image resemblance, creating a special visual region descriptor to obtain better visual feature generation for sketches and images, and a dynamic sketch-image matching scheme to achieve more precise characterization of the correlations between sketches and images. Such a visual-region-descriptor-based SBIR pattern can not only enable users to present whatever they imagine in their mind on the sketch query panel but also return the most similar images to the picture in users' mind. Very positive results were obtained in our experiments using a large quantity of public data. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2671188.2749302 | ICMR |
Keywords | Field | DocType |
Sketch-based image retrieval, visual region descriptor, sketch-like representation, feature generation, matching, dynamic gridding | Computer vision,Pattern recognition,Computer science,Image retrieval,Correlation distribution,Artificial intelligence,Feature generation,Binary number,Sketch,Visual Word | Conference |
Citations | PageRank | References |
5 | 0.44 | 23 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cheng Jin | 1 | 78 | 14.92 |
| Zheming Wang | 2 | 30 | 8.12 |
| Tianhao Zhang | 3 | 5 | 0.44 |
| Qinen Zhu | 4 | 5 | 0.44 |
| Yuejie Zhang | 5 | 127 | 25.82 |