Automatic attribute discovery and characterization from noisy web data

217Citations
Citations of this article
200Readers
Mendeley users who have this article in their library.

Abstract

It is common to use domain specific terminology - attributes - to describe the visual appearance of objects. In order to scale the use of these describable visual attributes to a large number of categories, especially those not well studied by psychologists or linguists, it will be necessary to find alternative techniques for identifying attribute vocabularies and for learning to recognize attributes without hand labeled training data. We demonstrate that it is possible to accomplish both these tasks automatically by mining text and image data sampled from the Internet. The proposed approach also characterizes attributes according to their visual representation: global or local, and type: color, texture, or shape. This work focuses on discovering attributes and their visual appearance, and is as agnostic as possible about the textual description. © 2010 Springer-Verlag.

References Powered by Scopus

Object detection with discriminatively trained part-based models

8618Citations
N/AReaders
Get full text

Learning to detect unseen object classes by between-class attribute transfer

2024Citations
N/AReaders
Get full text

Describing objects by their attributes

1666Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Image retrieval using scene graphs

877Citations
N/AReaders
Get full text

SUN attribute database: Discovering, annotating, and recognizing scene attributes

725Citations
N/AReaders
Get full text

Multimodal distributional semantics

680Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Berg, T. L., Berg, A. C., & Shih, J. (2010). Automatic attribute discovery and characterization from noisy web data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6311 LNCS, pp. 663–676). Springer Verlag. https://doi.org/10.1007/978-3-642-15549-9_48

Readers over time

‘10‘11‘12‘13‘14‘15‘16‘17‘18‘19‘20‘21‘22‘23‘24010203040

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 99

66%

Researcher 34

23%

Professor / Associate Prof. 12

8%

Lecturer / Post doc 4

3%

Readers' Discipline

Tooltip

Computer Science 138

86%

Engineering 17

11%

Agricultural and Biological Sciences 3

2%

Mathematics 3

2%

Save time finding and organizing research with Mendeley

Sign up for free
0