计算机科学 ›› 2014, Vol. 41 ›› Issue (12): 260-263.doi: 10.11896/j.issn.1002-137X.2014.12.056
吴丽娜,黄雅平,郑翔
WU Li-na,HUANG Ya-ping and ZHENG Xiang
摘要： 在分类新类别图像时,词袋模型总需要重新学习视觉词典及分类器,而不能充分利用已经学习好的视觉词典。运用迁移学习的思想,提出一种视觉短语的迁移学习算法。这种视觉短语不仅包含图像的局部不变特征,而且包含特征间的空间结构信息,能更有效地描述不同类别图像之间的共同特征。在分类新类别图像时,算法通过迁移视觉短语而不是重新学习视觉词典,来完成图像分类任务。实验结果证明这种迁移算法能有效地利用已有知识,在分类 新类别图像时取得很好的效果,而且还能适用于仅有少量训练样本的图像分类任务。
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