發(fā)布時間：2018-01-19 23:52

  本文關(guān)鍵詞： 圖文數(shù)據(jù) softmax多分類器 多分類支持向量機 加權(quán)KNN 融合分類方法　出處：《中國圖象圖形學報》2016年07期 　論文類型：期刊論文

【摘要】：目的圖文數(shù)據(jù)在不同應(yīng)用場景下的最佳分類方法各不相同,而現(xiàn)有語義級融合算法大多適用于圖文數(shù)據(jù)分類方法相同的情況,若將其應(yīng)用于不同分類方法時由于分類決策基準不統(tǒng)一導致分類結(jié)果不理想,大幅降低了融合分類性能。針對這一問題,提出基于加權(quán)KNN的融合分類方法。方法首先,分別利用softmax多分類器和多分類支持向量機(SVM)實現(xiàn)圖像和文本分類,同時利用訓練數(shù)據(jù)集各類別分類精確度加權(quán)后的圖像和文本正確判別實例的分類決策值分別構(gòu)建圖像和文本KNN模型;再分別利用其對測試實例的圖像和文本分類決策值進行預測,通過最鄰近k個實例屬于各類別的數(shù)目確定測試實例的分類概率,統(tǒng)一圖像和文本的分類決策基準;最后利用訓練數(shù)據(jù)集中圖像和文本分類正確的數(shù)目確定測試實例中圖像和文本分類概率的融合系數(shù),實現(xiàn)統(tǒng)一分類決策基準下的圖文數(shù)據(jù)融合。結(jié)果在Attribute Discovery數(shù)據(jù)集的圖像文本對上進行實驗,并與基準方法進行比較,實驗結(jié)果表明,本文融合算法的分類精確度高于圖像和文本各自的分類精確度,且平均分類精確度相比基準方法提高了4.45%;此外,本文算法對圖文信息的平均整合能力相比基準方法提高了4.19%。結(jié)論本文算法將圖像和文本不同分類方法的分類決策基準統(tǒng)一化,實現(xiàn)了圖文數(shù)據(jù)的有效融合,具有較強的信息整合能力和較好的融合分類性能。
[Abstract]:Objective the optimal classification methods of graphic and text data in different application scenarios are different, and most of the existing semantic level fusion algorithms are suitable for the same classification methods of graphic and text data. If it is applied to different classification methods, the classification result is not ideal due to the disunity of classification decision criteria, which greatly reduces the performance of fusion classification. A fusion classification method based on weighted KNN is proposed. Firstly, image and text classification are realized by using softmax multi-classifier and multi-classification support vector machine respectively. At the same time, the KNN model of image and text are constructed by using the classification decision value of the accurate classification accuracy of each category of training data set. Then we use it to predict the decision value of image and text classification of test cases, and determine the classification probability of test cases by the number of the nearest k instances belonging to different kinds of others. Unified image and text classification decision-making benchmark; Finally, using the correct number of image and text classification in the training data set, the fusion coefficient of the probability of image and text classification in test examples is determined. The results are compared with the benchmark method and the experimental results are carried out on the image text pairs of the Attribute Discovery data set. The experimental results show that the classification accuracy of the fusion algorithm is higher than that of image and text, and the average classification accuracy is 4.45% higher than the baseline method. In addition, the average integration ability of the algorithm is 4.19% higher than that of the benchmark method. Conclusion the algorithm unifies the classification decision benchmark of different image and text classification methods. It realizes the effective fusion of graph and text data, and has strong ability of information integration and better performance of fusion and classification.
【作者單位】： 中國科學院電子學研究所;中國科學院大學;
【基金】：國家自然科學基金項目(41301493) 高分對地觀測領(lǐng)域?qū)W術(shù)交流基金項目(GFEX04060103)~~
【分類號】：TP391.41;TP18
【正文快照】： 0引言 隨著互聯(lián)網(wǎng)技術(shù)的發(fā)展,數(shù)據(jù)量呈現(xiàn)爆炸式增長,數(shù)據(jù)類型不再局限于單一的文本,而是擴展到圖像、音頻、視頻等多媒體數(shù)據(jù)。其中圖像以其豐富的視覺特征,將抽象數(shù)據(jù)直觀、生動、形象的呈現(xiàn)給人們,使得信息的傳播和交流更為便捷�；ヂ�(lián)網(wǎng)多媒體數(shù)據(jù)規(guī)模大、類型多、組織結(jié)構(gòu)，

本文編號：1446001