Name
Abstract | ||
|---|---|---|
Visual attributes, as a bridge between humans and machines, have played an important role in various applications. We focus on attribute-based image retrieval. Unlike early works that either initiate the image search via binary attributes or refine the search results via relative attributes, we propose to realize image retrieval using grouped attribute strength, which are learned from both binary attributes and relative attributes. First, early methods are employed to predict the binary attributes and sort the relative attributes in descending order in different situations. Second, we group the sorted images corresponding to each attribute and combine the learned binary and relative attributes to learn the grouped attribute strength of the images, which result in a new attribute feature vector for each image. Finally, we apply the learned grouped attribute strength to different retrieval models to realize our image retrieval tasks. We demonstrate the approach on LFW-10 and Shoes datasets and show its clear advantages over traditional binary attribute-based retrieval methods. (C) 2019 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/1.JEI.28.1.013048 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
image retrieval,binary attribute,relative attribute,grouped attribute strength | Feature vector,Pattern recognition,Computer science,sort,Image retrieval,Artificial intelligence,Binary number | Journal |
Volume | Issue | ISSN |
28 | 1 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fen Zhang | 1 | 0 | 0.68 |
| Xiang-Wei Kong | 2 | 212 | 15.09 |
| Ze Jia | 3 | 0 | 0.34 |