This paper presents a scalable scene parsing algorithm based on image retrieval and superpixel matching. We focus on rare object classes, which play an important role in achieving richer semantic understanding of visual scenes, compared to common background classes. Towards this end, we make two novel contributions: rare class expansion and semantic context description. First, considering the long-tailed nature of the label distribution, we expand the retrieval set by rare class exemplars and thus achieve more balanced superpixel classification results. Second, we incorporate both global and local semantic context information through a feedback based mechanism to refine image retrieval and superpixel matching. Results on the SIFTflow and LMSun datasets show the superior performance of our algorithm, especially on the rare classes, without sacrificing overall labeling accuracy.
|Title of host publication||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Publisher||IEEE Computer Society|
|Number of pages||8|
|ISBN (Electronic)||9781479951178, 9781479951178|
|Publication status||Published - 2014 Sept 24|
|Event||27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States|
Duration: 2014 Jun 23 → 2014 Jun 28
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Other||27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014|
|Period||14/6/23 → 14/6/28|
Bibliographical notePublisher Copyright:
© 2014 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition