.png){kind=icon}
Return
Nonnegative correlation coding for image classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s11432-015-5289-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Feature coding is one of the most important procedures in the bag-of-features model for image classification. In this paper, we propose a novel feature coding method called nonnegative correlation coding. In order to obtain a discriminative image representation, our method employs two correlations: the correlation between features and visual words, and the correlation between the obtained codes. The first correlation reflects the locality of codes, i.e., the visual words close to the local feature are activated more easily than the ones distant. The second correlation characterizes the similarity of codes, and it means that similar local features are likely to have similar codes. Both correlations are modeled under the nonnegative constraint. Based on the Nesterov's gradient projection algorithm, we develop an effective numerical solver to optimize the nonnegative correlation coding problem with guaranteed quadratic convergence. Comprehensive experimental results on publicly available datasets demonstrate the effectiveness of our method.
Keywords:
image classification
correlation coding
nonnegativity
locality
similarity
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
4.9K
Citations:
8.9K
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSaveShareSave
View more