基于紋元森林和顯著性先驗(yàn)的弱監(jiān)督圖像語(yǔ)義分割方法

韓錚; 肖志濤

doi:10.11999/JEIT170472

基于紋元森林和顯著性先驗(yàn)的弱監(jiān)督圖像語(yǔ)義分割方法

doi: 10.11999/JEIT170472

韓錚¹,
肖志濤²

2.
(天津工業(yè)大學(xué)電子與信息工程學(xué)院天津 300387)

基金項(xiàng)目:

高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金(SRFDP 20131201110001)，中國(guó)紡織工業(yè)協(xié)會(huì)應(yīng)用基礎(chǔ)研究項(xiàng)目(J201509)，內(nèi)蒙古自治區(qū)高等學(xué)?？茖W(xué)技術(shù)研究項(xiàng)目(NJZY237)

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出版歷程
- 收稿日期: 2017-05-17
- 修回日期: 2017-11-27
- 刊出日期: 2018-03-19

Weakly Supervised Semantic Segmentation Based on Semantic Texton Forest and Saliency Prior

HAN Zheng¹,
XIAO Zhitao²

2.
(School of Electronics and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China)

Funds:

The Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP20131201110001), The Applied Basic Research Programs of China National Textile and Apparel Council (J201509), The Scientific Studies Program of Higher Education of Inner Mongolia Municipality (NJZY237)

摘要

摘要: 弱監(jiān)督語(yǔ)義分割任務(wù)常利用訓(xùn)練集中全體圖像的超像素及其相似度建立圖模型，使用圖像級(jí)別標(biāo)記的監(jiān)督關(guān)系進(jìn)行約束求解。全局建模缺少單幅圖像結(jié)構(gòu)信息，同時(shí)此類參數(shù)方法受到復(fù)雜度限制，無(wú)法使用大規(guī)模的弱監(jiān)督訓(xùn)練數(shù)據(jù)。針對(duì)以上問題，該文提出一種基于紋元森林和顯著性先驗(yàn)的弱監(jiān)督圖像語(yǔ)義分割方法。算法使用弱監(jiān)督數(shù)據(jù)和圖像顯著性訓(xùn)練隨機(jī)森林分類器用于語(yǔ)義紋元森林特征(Semantic Texton Forest, STF)的提取。測(cè)試時(shí)，先將圖像進(jìn)行過分割，然后提取超像素語(yǔ)義紋元特征，利用樸素貝葉斯法進(jìn)行超像素標(biāo)記的概率估計(jì)，最后在條件隨機(jī)場(chǎng)(CRF)框架下結(jié)合圖像顯著性信息定義了新的能量函數(shù)表達(dá)式，將圖像的標(biāo)注(labeling)問題轉(zhuǎn)換為能量最小化問題求解。在MSRC-21類數(shù)據(jù)庫(kù)上進(jìn)行了驗(yàn)證，完成了語(yǔ)義分割任務(wù)。結(jié)果表明，在并未對(duì)整個(gè)訓(xùn)練集建立圖模型的情況下，僅利用單幅圖像的顯著性信息也可以得到較好的分割結(jié)果，同時(shí)非參模型有利于規(guī)模數(shù)據(jù)分析。
- 語(yǔ)義分割 /
- 弱監(jiān)督學(xué)習(xí) /
- 顯著性檢測(cè) /
- 語(yǔ)義紋元森林 /
- 條件隨機(jī)場(chǎng)
Abstract: Most previous weakly supervised semantic segmentation works utilize the labels of the whole training set and thereby need the construction of a relationship graph about image labels. This method lack of structure information in single image and suffer from enormous quantity parameters which result in expensive computation. In this study, a weakly-supervised semantic segmentation algorithm is proposed. Under Conditional Random Field (CRF) framework, an novel energy function expression is developed based on saliency priors as structure context relationship, which avoids the construction of a huge graph in whole training dataset. Specifically, a nonparametric random Semantic Texton Forest (STF) is obtained using weakly supervised training data and images saliency. Then STF feature is extracted from image superpixels and probability estimates of superpixels label is calculated by naive Bayesian method. Finally, a CRF based optimization algorithm is proposed which can efficiency solved by alpha expansion algorithm. Experiments on the MSRC-21 dataset show that the new algorithm outperforms some previous influential weakly-supervised segmentation algorithms with no building graph in whole training set.
- Semantic segmentation /
- Weakly supervised learning /
- Saliency detection /
- Semantic Texton Forest (STF) /
- Conditional Random Fields (CRF)

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參考文獻(xiàn)(24)

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