Name
Abstract | ||
|---|---|---|
Category based image search, where the goal is to retrieve images of a specific category from a large database, is becoming increasingly popular. In such a setting, the query is often a classifier. However, the complexity of the classifiers (often SVMs) used for this purpose hinders the use of such a solution in practice. Problem becomes paramount when the database is huge and/or the dimensionality of the feature representation is also very large. In this paper, we address this issue by proposing a novel method which decomposes the query classifier into set of known eigen queries. We use their precomputed results (or scores) for computing the ranked list corresponding to novel queries. We also propose an approximate algorithm which accesses only a fraction of the data to perform fast retrieval. Experiments on various datasets show that our method reports high accuracy and efficiency. Apart from retrieval, the proposed method can also be used to discover interesting new concepts from the given dataset. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-37444-9_36 | ACCV |
Keywords | Field | DocType |
large database,novel method,fast retrieval,novel query,eigen query,image retrieval,feature representation,query classifier,approximate algorithm,specific category | Data mining,Feature vector,Ranking,Pattern recognition,Computer science,Support vector machine,Image retrieval,Curse of dimensionality,Artificial intelligence,Classifier (linguistics) | Conference |
Citations | PageRank | References |
0 | 0.34 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nisarg Raval | 1 | 68 | 5.85 |
| Rashmi Vilas Tonge | 2 | 0 | 0.34 |
| C. V. Jawahar | 3 | 1700 | 148.58 |