Rapid object search engine for contextual advertisement
Proceedings of the 20th ACM international conference on Multimedia
Randomized spatial partition for scene recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Arbitrary-Shape object localization using adaptive image grids
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Spatially aware feature selection and weighting for object retrieval
Image and Vision Computing
Discriminative Hough context model for object detection
The Visual Computer: International Journal of Computer Graphics
| Hi-index | 0.00 |
Accurate matching of local features plays an essential role in visual object search. Instead of matching individual features separately, using the spatial context, e.g., bundling a group of co-located features into a visual phrase, has shown to enable more discriminative matching. Despite previous work, it remains a challenging problem to extract appropriate spatial context for matching. We propose a randomized approach to deriving visual phrase, in the form of spatial random partition. By averaging the matching scores over multiple randomized visual phrases, our approach offers three benefits: 1) the aggregation of the matching scores over a collection of visual phrases of varying sizes and shapes provides robust local matching; 2) object localization is achieved by simple thresholding on the voting map, which is more efficient than subimage search; 3) our algorithm lends itself to easy parallelization and also allows a flexible trade-off between accuracy and speed by adjusting the number of partition times. Both theoretical studies and experimental comparisons with the state-of-the-art methods validate the advantages of our approach.