Name
Abstract | ||
|---|---|---|
Most of the Nearest Neighbor (NN) based image annotation (or classification) methods cannot achieve satisfactory performance. In this paper, we propose a novel Nearest Neighbor method based on a multi-feature distance metric, which takes full advantage of different and complementary features. We first establish a metric for each feature and assign a weight for every metric, and then linearly combine all of them together to form one distance metric, namely the multi-feature metric. After that, we construct an NN model based on "image-to-cluster" distances, which equals to the distances between an image and the clusters within an image category using our multi-feature based metric, and which is different from calculating Euclidean distances between two images. By introducing this multi-feature based distance metric, our NN based model can mitigate the semantic issues due to intra-class variations and inter-class similarities, and improve the image annotation performance. Experiments confirm the superiority of our model in comparison with both the traditional classifiers and the state of the art learning-based models. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26561-2_57 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Nearest neighbor,Distance metric learning,Multi-feature metric,Image annotation | k-nearest neighbors algorithm,Locality-sensitive hashing,Automatic image annotation,Pattern recognition,Best bin first,Computer science,Metric (mathematics),Nearest neighbor graph,Artificial intelligence,Cover tree,Nearest neighbor search | Conference |
Volume | ISSN | Citations |
9492 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wu Wei | 1 | 204 | 14.84 |
| Guanglai Gao | 2 | 78 | 24.57 |