Paper Info
Title
Semi-Supervised Bi-Dictionary Learning for Image Classification With Smooth Representation-Based Label Propagation.
Abstract
In this paper, we propose semi-supervised bi-dictionary learning for image classification with smooth representation-based label propagation (SRLP). Natural images contain complex contents of multiple objects with complicated background, clutter, and occlusions, which prevents image features from belonging to a specific category. Therefore, we employ reconstruction-based classification to implement discriminative dictionary learning in a probabilistic manner. We jointly learn a discriminative dictionary called anchor in the feature space and its corresponding soft label called anchor label in the label space, where the combination of anchor and anchor label is referred to as bi-dictionary. The learnt bi-dictionary is utilized to bridge the semantic gap in image classification. First, SRLP constructs smoothed reconstruction problems for bi-dictionary learning. Then, SRLP produces the reconstruction coefficients in the feature space over the anchor to infer soft labels of samples in the label space. Experimental results demonstrate that the proposed method is capable of learning a pair of discriminative dictionaries for image classification in the feature and label spaces and outperforms the-state-of-the-art reconstruction-based classification ones.
Year
DOI
Venue
2016
10.1109/TMM.2016.2515367
IEEE Trans. Multimedia
Keywords
Field
DocType
Image reconstruction,Semantics,Collaboration,Support vector machines,Bridges
Iterative reconstruction,Computer vision,Feature vector,Pattern recognition,Computer science,Feature (computer vision),Semantic gap,Support vector machine,Artificial intelligence,Probabilistic logic,Contextual image classification,Discriminative model
Journal
Volume
Issue
ISSN
18
3
1520-9210
Citations 
PageRank 
References 
9
0.45
25
Authors
2
Name
Order
Citations
PageRank
Meng Jian1598.07
Cheolkon Jung234247.75