【摘要】：以素描教學(xué)過程中的臨摹作品作為研究對象,將深度卷積特征應(yīng)用于素描作品的分類與評價中.首先測試深度卷積特征在素描作品分類中的效果,同時將素描作品評價問題轉(zhuǎn)換為基于作品的構(gòu)圖、形準(zhǔn)、質(zhì)感、畫面整體黑白灰等圖像高階語義特征的細分類問題(優(yōu)、良、中、差);然后提出雙線性卷積模型,以較好地解決圖像細分類問題;最后使用Tensor Sketch投影算法將雙線性深度卷積特征進行壓縮,并采用端到端的訓(xùn)練進行模型微調(diào).實驗結(jié)果表明,在素描作品分類任務(wù)中,深度卷積特征明顯優(yōu)于傳統(tǒng)手工特征(如直方圖特征、紋理特征和SIFT特征);在素描作品評價中,壓縮的雙線性深度卷積特征能在較低維度上達到相似的評價效果.
[Abstract]:The deep convolution feature is applied to the classification and evaluation of sketch works by taking the copying works in the process of sketch teaching as the research object. In this paper, we first test the effect of deep convolution feature in the classification of sketch works. At the same time, the evaluation problem of sketch works is transformed into the fine classification of high-order semantic features of images such as composition, accuracy, texture, whole picture, black and white, and so on. Medium, poor); Then the bilinear convolution model is proposed to solve the problem of image fine classification. Finally the bilinear depth convolution feature is compressed by Tensor Sketch projection algorithm and the end-to-end training is used to fine-tune the model. The experimental results show that the depth convolution feature is superior to the traditional manual feature (such as histogram feature, texture feature and SIFT feature) in the classification of sketch works. In the evaluation of sketch works, the compressed bilinear deep convolution features can achieve similar evaluation results in lower dimensions.
【作者單位】： 浙江大學(xué)計算機科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金(61562072,61303137,61402141) 教育部博士點基金(20130101110148)
【分類號】：TP391.41

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