Title
The segmented and annotated IAPR TC-12 benchmark
Author
HUGO JAIR ESCALANTE BALDERAS
CARLOS ARTURO HERNANDEZ GRACIDAS
JESUS ANTONIO GONZALEZ BERNAL
AURELIO LOPEZ LOPEZ
MANUEL MONTES Y GOMEZ
EDUARDO FRANCISCO MORALES MANZANARES
LUIS ENRIQUE SUCAR SUCCAR
LUIS VILLASEÑOR PINEDA
Access level
Open Access
Subjects
Data set creation - (DATA SET CREATION) Ground truth collection - (GROUND TRUTH COLLECTION) Evaluation metrics - (EVALUATION METRICS) Automatic image annotation - (AUTOMATIC IMAGE ANNOTATION) Image retrieval - (IMAGE RETRIEVAL) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)
Summary or description
Automatic image annotation (AIA), a highly popular topic in the field of information retrieval research, has experienced significant progress within the last decade. Yet, the lack of a standardized evaluation platform tailored to the needs of AIA, has hindered effective evaluation of its methods, especially for region-based AIA. Therefore in this paper, we introduce the segmented and annotated IAPR TC-12 benchmark; an extended resource for the evaluation of AIA methods as well as the analysis of their impact on multimedia information retrieval. We describe the methodology adopted for the manual segmentation and annotation of images, and present statistics for the extended collection. The extended collection is publicly available and can be used to evaluate a variety of tasks in addition to image annotation. We also propose a soft measure for the evaluation of annotation performance and identify future research areas in which this extended test collection is likely to make a contribution.
Publisher
Elsevier Inc.
Publish date
2010
Publication type
Article
Publication version
Accepted Version
Information Resource
Format
application/pdf
Language
English
Audience
Students
Researchers
General public
Citation suggestion
Escalante-Balderas, H.J., et al., (2010). The segmented and annotated IAPR TC-12 benchmark, Computer Vision and Image Understanding, (114): 419–428
Source repository
Repositorio Institucional del INAOE
Downloads
90