Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4944756 | Information Sciences | 2016 | 37 Pages |
Abstract
This paper presents a novel image feature called Gradient Orientation Consistency (GOC) and an image representation based on the spatial multi-scale GOC histogram for place instance and scene category recognition. Gradient orientations have proven to be a more robust descriptor of the image information than intensities, in the presence of illumination variances. The GOC feature is obtained by imposing a Local Binary Pattern (LBP) type of non-parametric operator to the gradient orientation codes, hence captures the local structures in an image. The histogram of GOC further encodes the global structure of the entire image. The multi-scale analysis and spatial pyramid techniques are also used to enrich the longer-range structure information, resulting in a comprehensive image representation. We apply this representation method for place instance and scene category recognition on five different datasets and show that it outperforms the state of the art.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Changxin Gao, Nong Sang, Rui Huang,