【摘要】：提出經(jīng)前饋神經(jīng)網(wǎng)絡快速在線學習、構(gòu)建像素分類模型進行圖像分割的算法.首先利用譜殘差法計算像素顯著度,通過對少數(shù)高顯著度點的分布進行多尺度分析,獲得符合人眼視覺特性的顯著圖和注視區(qū)域.然后從注視區(qū)域和非注視區(qū)域隨機抽樣構(gòu)成由正負樣本像素組成的訓練集,在線訓練一個兩分類的隨機權(quán)前饋神經(jīng)網(wǎng)絡模型.最后使用該模型分類全圖像素,實現(xiàn)圖像分割.實驗表明,文中算法在譜殘差法基礎上提升對圖像中顯著目標的分割性能,分割結(jié)果與人類視覺感知匹配度較好.
[Abstract]:A fast online learning algorithm based on feedforward neural network is proposed to construct pixel classification model for image segmentation. Firstly, the spectral residual method is used to calculate the pixel saliency. Through the multi-scale analysis of the distribution of a small number of high saliency points, the salient map and fixation region in accordance with the visual characteristics of the human eye are obtained. Then a training set composed of positive and negative sample pixels is constructed from the random sampling of the fixation region and the non-fixation region, and a two-classification stochastic weight feedforward neural network model is trained online. Finally, the model is used to classify the whole picture pixels, and the image segmentation is realized. The experimental results show that the proposed algorithm improves the segmentation performance of significant targets in the image on the basis of spectral residual method, and the segmentation results match well with human visual perception.
【作者單位】： 中國計量大學信息工程學院;
【基金】：國家自然科學基金項目(No.61572449) 浙江省自然科學基金項目(No.LY13F010004)資助~~
【分類號】：TP391.41
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本文編號：2515058