.png){kind=icon}
Return
Scene image classification using locality-constrained linear coding based on histogram intersection
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-017-4830-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recently linear Spatial Pyramid Matching (SPM) method based on sparse coding has achieved great success in image classification. The raise of Locality-constrained Linear Coding (LLC) proves the importance of locality. In this paper, we propose an improved feature coding scheme called Locality-constrained Linear Coding Based on Histogram Intersection (HILLC). HILLC uses histogram intersection to describe the distance between feature vector and codebook. For each feature vector, search the KNN nearest neighbors to construct a local codebook. Compared with LLC, HILLC can obtain more robust codes. Experimental results demonstrate that our proposed method outperforms other related coding methods.
Keywords:
SPM
Sparse coding
Histogram intersection
Image classification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
1.9W
Citations:
3.2W
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSave
ShareSaveShareSaveShareSaveShareSave
View more