Due to interclass and intraclass variations, the images of different classes are often cluttered which makes it hard for efficient classifications. The use of discriminative classification algorithms helps to alleviate this problem. However, it is still an open problem to accurately model the relationships between visual representations and human perception. To alleviate these problems, in this paper, we propose a novel multiview semantic representation (MVSR) algorithm for efficient visual recognition. First, we leverage visually based methods to get initial image representations. We then use both visual and semantic similarities to divide images into groups which are then used for semantic representations. We treat different image representation strategies, partition methods, and numbers as different views. A graph is then used to combine the discriminative power of different views. The similarities between images can be obtained by measuring the similarities of graphs. Finally, we train classifiers to predict the categories of images. We evaluate the discriminative power of the proposed MVSR method for visual recognition on several public image datasets. Experimental results show the effectiveness of the proposed method.

中文翻译：

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视觉识别的多视图语义表示


由于类间和类内的差异，不同类的图像通常很混乱，这使得有效的分类变得困难。判别性分类算法的使用有助于缓解这个问题。然而，准确地模拟视觉表示和人类感知之间的关系仍然是一个悬而未决的问题。为了缓解这些问题，在本文中，我们提出了一种新颖的多视图语义表示（MVSR）算法，用于有效的视觉识别。首先，我们利用基于视觉的方法来获得初始图像表示。然后，我们使用视觉和语义相似性将图像分组，然后将其用于语义表示。我们将不同的图像表示策略、分区方法和数字视为不同的视图。然后使用图表来结合不同观点的辨别力。图像之间的相似度可以通过测量图的相似度来获得。最后，我们训练分类器来预测图像的类别。我们评估了所提出的 MVSR 方法在多个公共图像数据集上进行视觉识别的判别能力。实验结果表明了该方法的有效性。