Abstract:
Hand-engineered local image features have been proven to be intended representation for a variety of high-level visual recognition tasks. But as the visual recognition ta...View moreMetadata
Abstract:
Hand-engineered local image features have been proven to be intended representation for a variety of high-level visual recognition tasks. But as the visual recognition tasks such as scene classification and object detection become more challenging, the semantic gap between low-level feature and the concept descriptor of the scene images increases. In this paper, we present novel semantic multinomial (SMN) image representation that renders it possible to represent natural scenes by complex semantic description. SMN is a semantic representation of an image that corresponds to a vector of posterior probabilities of concepts. Proposed SMN representation uses dynamic kernel based support vector machines (SVMs) to model the semantic content of images. It is necessary to have ground truth (true) concept labels to obtain SMN representation. In this work, we also propose to use pseudo-concepts in the absence of true concept labels. The proposed SMN representation is also complementary to the low-level visual representation. Combining the scores of classifiers using SMN representation and low-level visual representation is shown to achieve state-of-the-art results for high-level visual tasks such as scene classification on standard datasets.
Date of Conference: 02-04 March 2017
Date Added to IEEE Xplore: 23 October 2017
ISBN Information: