首页> 外文会议>Computational Intelligence, Modelling and Simulation, 2009. CSSim '09 >Using Patch Histogram of Detected Corners for Image Retrieval
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Using Patch Histogram of Detected Corners for Image Retrieval

机译:使用检测到的角点的直方图直方图进行图像检索

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In this paper, we proposed an algorithm which extracts patches from corner points for calculating their histogram. Then that histogram is used to construct a feature vector for representing an image and subsequently for searching the similar images from image database. The corners are detected and extracted by finding the intersections of the detected lines. Lines are identified by using Hough transform. Before detecting the lines we have to find out the edges information from the image, this is done by using canny edge detection algorithm. Taking the corner as the center coordinate, the 9 × 9 pixel patch is extracted to get the pixel information around the corner is used to calculate the histogram. This result in a significant small size feature matrix compared to the algorithms using color features. Experimental results show that it is computationally efficient and the image corners being noise invariant produce good results in noisy environments.
机译:在本文中,我们提出了一种算法,该算法从角点提取补丁以计算其直方图。然后,该直方图用于构造特征向量,以表示图像并随后从图像数据库中搜索相似图像。通过找到检测到的线的交点来检测和提取拐角。通过使用霍夫变换来识别线。在检测线条之前,我们必须从图像中找出边缘信息,这是通过使用Canny边缘检测算法完成的。以拐角为中心坐标,提取9×9像素块以获得拐角附近的像素信息,以计算直方图。与使用颜色特征的算法相比,这会导致显着的小尺寸特征矩阵。实验结果表明,该算法具有较高的计算效率,并且在嘈杂的环境中,图像角不变且噪声不变。

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