.png){kind=icon}
返回
Image Similarity Using Sparse Representation and Compression Distance
.png){kind=icon}
OA
AI
DOI：10.1109/TMM.2014.2306175.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A new line of research uses compression methods to measure the similarity between signals. Two signals are considered similar if one can be compressed significantly when the information of the other is known. The existing compression-based similarity methods, although successful in the discrete one dimensional domain, do not work well in the context of images. This paper proposes a sparse representation-based approach to encode the information content of an image using information from the other image, and uses the compactness (sparsity) of the representation as a measure of its compressibility (how much can the image be compressed) with respect to the other image. The sparser the representation of an image, the better it can be compressed and the more it is similar to the other image. The efficacy of the proposed measure is demonstrated through the high accuracies achieved in image clustering, retrieval and classification.
Keyword:
Compression
image similarity
Kolmogorov complexity
overcomplete dictionary
sparse representation
期刊
IF:
9.7
论文数:
4.7K
被引数:
2.4W
机构
引用论文
Race, Ethnicity, Income Concentration and 10-Year Change in Urban Greenness in the United States
OAAI
.png){kind=icon}
OAAI分享收藏A micromachined efficient parametric array loudspeaker with a wide radiation frequency band具有宽辐射频带的微机械高效参量阵列扬声器
PREAI
分享收藏分享收藏
查看更多内容