Proceedings of the ACM International Conference on Image and Video Retrieval 2009
DOI: 10.1145/1646396.1646434
|View full text |Cite
|
Sign up to set email alerts
|

Context-based multi-label image annotation

Abstract: This paper presents a novel context-based keyword propagation method for automatic image annotation. We follow the idea of keyword propagation and formulate image annotation as a multi-label learning problem, which is further resolved efficiently by linear programming. In this way, our method can exploit the context between keywords during keyword propagation. Unlike the popular relevance models that treat each keyword independently, our method can simultaneously propagate multiple keywords (i.e. labels) from … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
20
0

Year Published

2010
2010
2016
2016

Publication Types

Select...
3
3
2

Relationship

0
8

Authors

Journals

citations
Cited by 24 publications
(21 citation statements)
references
References 20 publications
0
20
0
Order By: Relevance
“…Each word might have several dictionary senses that are visually distinct, which is known as the 'visual polysemous' property. Therefore, image annotation can be transformed into a multi-class classification task, which can be solved using support vector machines (SVMs) or multi-label learning algorithms (Lu et al 2009) and multi-instance learning algorithms (Zhou and Zhang 2006). The co-occurrence probability between regional image features and concepts can be estimated to produce annotations for images.…”
Section: Image Annotationmentioning
confidence: 99%
“…Each word might have several dictionary senses that are visually distinct, which is known as the 'visual polysemous' property. Therefore, image annotation can be transformed into a multi-class classification task, which can be solved using support vector machines (SVMs) or multi-label learning algorithms (Lu et al 2009) and multi-instance learning algorithms (Zhou and Zhang 2006). The co-occurrence probability between regional image features and concepts can be estimated to produce annotations for images.…”
Section: Image Annotationmentioning
confidence: 99%
“…For the application of image annotation task, we use MSRC dataset for evaluation, which is a new dataset but widely used in recent years [8,10,13,15,18]. This dataset consists of 591 images assigned to 21 predefined labels (As suggested by MSRC, the label "horse" and "mountain" are ignored due to a limited number of labeled images).…”
Section: Image Annotation On Msrc Datasetmentioning
confidence: 99%
“…In addition, we compare our method to related methods like MBRM [4] and SSK [10] by F1 score, since this evaluation metric is commonly used in automatic image annotation studies and has been used by others in this dataset. It measures the quality of retrieved relevant images with each keyword, which is treated as class label here.…”
Section: Image Annotation On Msrc Datasetmentioning
confidence: 99%
See 1 more Smart Citation
“…Therefore, extracting only the highlight of an image (or the main object of an image) is essential to improve classification accuracy. Here, an image method called JSEG [21] is used to segment the objects in an image. The JSEG method consists of two steps, color space quantization and spatial segmentation.…”
Section: Segmentation and Main Object Detectionmentioning
confidence: 99%