Name
Abstract | ||
|---|---|---|
The most popular approach to large scale image retrieval is based on the bag-of-word (BoW) representation of images. There is an important trick how the statistical and geometry features of BoW are efficiently used. We present a two-step approach for image retrieval with statistical features and spatial geometry information been considered in different step. In the first step, the statistical features of the images' BoW are achieved to capture the underlying image topic to screen those images. In the second step, images from same topic are ranked using the concept of co-occurrence features (a type of geometry features). Computational cost of the retrieval is reduced because the first step does not consider computing the expensive spatial geometry information and the second step only uses significant features to rank images. Experiments on the Oxford 5K benchmark show that the proposed technique can stably achieve nearly the same result compared with a state-of-the-art retrieval method [1] while only spending about a tenth of the time the method takes. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-14364-4_69 | ADVANCES IN VISUAL COMPUTING (ISVC 2014), PT II |
Field | DocType | Volume |
Computer vision,Pattern recognition,Ranking,Computer science,Image retrieval,Artificial intelligence,Geometry,Visual Word | Conference | 8888 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Liu | 1 | 351 | 52.21 |
| Liang-Bing Feng | 2 | 0 | 0.34 |
| Xing Wang | 3 | 0 | 0.34 |
| Ning Guo | 4 | 351 | 35.26 |