.png){kind=icon}
Return
Texture classification using spectral histograms
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2003.812327.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Based on a local spatial/frequency representation,we employ a spectral histogram as a feature statistic for texture classification. The spectral histogram consists of marginal distributions of responses of a bank of filters and encodes implicitly the local structure of images through the filtering stage and the global appearance through the histogram stage. The distance between two spectral histograms is measured using chi(2)-statistic. The spectral histogram with the associated distance measure exhibits several properties that are necessary for texture classification. A filter selection algorithm is proposed to maximize classification performance of a given dataset. Our classification experiments using natural texture images reveal that the spectral histogram representation provides a robust feature statistic for textures and generalizes well. Comparisons show that our method produces a marked improvement in classification performance. Finally we point out the relationships between existing texture features and the spectral histogram, suggesting that the latter may provide a unified texture feature.
Keywords:
filtering
spectral histogram
texture analysis
texture 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:
13.7
Papers:
1.0W
Citations:
8.4W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveExploring the causality links between financial markets and foreign direct investment in Africa
PREAI
ShareSaveShareSaveShareSave
View more