@inproceedings{fd7545affa8e424995b3474f67eac813,
title = "Automatic image annotation using inverse maps from semantic embeddings",
abstract = "Human annotation in large scale image databases is time-consuming and error-prone. Since it is very hard to mine image databases using just visual features or textual descriptors, it is common to transform the image features into a semantically meaningful space. In this paper, we propose to perform image annotation in a semantic space inferred based on sparse representations. By constructing a semantic embedding for the visual features, that is constrained to be close to the tag embedding, we show that a robust inverse map can be used to predict the tags. Experiments using standard datasets show the effectiveness of the proposed approach in automatic image annotation when compared to existing methods.",
keywords = "Image annotation, RBF interpolation, embedding, inverse map, sparse coding",
author = "Thiagarajan, {J. J.} and Ramamurthy, {K. N.} and P. Sattigeri and Bremer, {P. T.} and Andreas Spanias",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.",
year = "2014",
month = jan,
day = "28",
doi = "10.1109/ICIP.2014.7025628",
language = "English (US)",
series = "2014 IEEE International Conference on Image Processing, ICIP 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3107--3111",
booktitle = "2014 IEEE International Conference on Image Processing, ICIP 2014",
}