Many content-based image retrieval (CBIR) methods are being developed to store more and more information about images in shorter feature vectors and to improve image retrieval rate. In the proposed method, two-step approach to CBIR has been developed. The first step generates an image mask from local binary pattern (LBP). This LBP mask is then utilized to draw comparison between the centre pixel and the eight surrounding pixels. The second step involves drawing the peak and valley patterns of local directional binary pattern for each image which is then combined with the colour histogram to retrieve similar images. Existing methods suffer from lower average image retrieval accuracy even with larger feature vectors. The proposed method overcomes such problems through shorter feature vectors that can store more information about the image. As illustrated through experimental results, the proposed method produces promising results with shorter feature vector of length 56 and improved image retrieval rate of about 5–10%. Our method outperforms similar techniques when tested with public data sets.

中文翻译：

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使用局部定向峰谷二值模式检索颜色和纹理图像

正在开发许多基于内容的图像检索（CBIR）方法，以将越来越多的图像信息存储在较短的特征向量中，并提高图像检索率。在提出的方法中，已经开发出了两步式CBIR方法。第一步是根据本地二进制图案（LBP）生成图像蒙版。然后使用该LBP蒙版在中心像素和八个周围像素之间进行比较。第二步涉及为每个图像绘制局部定向二进制模式的峰值和谷值模式，然后将其与颜色直方图组合以检索相似的图像。现有方法即使具有较大的特征向量也具有较低的平均图像检索精度。所提出的方法通过可存储更多有关图像信息的较短特征向量克服了此类问题。如实验结果所示，所提出的方法以较短的长度为56的特征向量和约5-10％的改进图像检索率产生了有希望的结果。当使用公共数据集进行测试时，我们的方法优于类似技术。