Paper Info
Title
Composite Descriptors And Deep Features Based Visual Phrase For Image Retrieval
Abstract
Local descriptors are very effective features in bag-of-visualwords (BoW) and vector of locally aggregated descriptors (VALD) models for image retrieval. Different kinds of local descriptors represent different visual content. We recognize that spatial contextual information play an important role in image matching, image retrieval and image recognition. Therefore, to explore efficient features, firstly, a new local composite descriptor is proposed, which combines the advantages of SURF and color name (CN) information. Then, VLAD method is used to encode the proposed composite descriptors to a vector. Third, local deep features are extracted and fused with the encoded vector in the image block. Finally, to implement efficient retrieval system, a novel image retrieval framework is organized a novel image retrieval framework is organized based on the proposed feature fusion strategies. The proposed methods areis verified on three benchmark datasets, i.e., Holidays, Oxford5k and Ukbench. Experimental results show that our methods achieves good performance. Eespecially, the mAP and N-S score achieve 0.8281 and 3.5498 on Holidays and Ukbench datasets, respectively.
Year
DOI
Venue
2018
10.1007/978-3-030-00021-9_43
CLOUD COMPUTING AND SECURITY, PT VI
Keywords
Field
DocType
Composite descriptors, Visual phrase, Vector of locally aggregated descriptors, Feature fusion, Deep feature, Image retrieval
Color term,ENCODE,Contextual information,Feature fusion,Pattern recognition,Computer science,Image matching,Image retrieval,Phrase,Real-time computing,Artificial intelligence
Conference
Volume
ISSN
Citations 
11068
0302-9743
0
PageRank 
References 
Authors
0.34
15
6
Name
Order
Citations
PageRank
Yanhong Wang182.84
Linna Zhang2164.33
Yigang Cen311620.90
Ruizhen Zhao423514.23
Tingting Chai511.37
Yi Cen6101.84