Question

求高手帮帮忙翻译一下！谢谢啦！

Framework for image retrieval using machine learning and statistical similarity matching techniques
Abstract: The aim of this study is to take advantage of both shape and texture properties of image to improve the performance of
image indexing and retrieval algorithm. Further, a framework for partitioning image into non-overlapping tiles of different sizes,
which results in higher retrieval efficiency, is presented. In the new approach, the image is divided into different regions (tiles).
Then, the energy and standard deviation of Hartley transform coefficients of each tile, which serve as the local descriptors of
texture, are extracted as sub-features. Next, invariant moments of edge image are used to record the shape features. The shape
features and combination of sub-features of texture provide a robust feature set for image retrieval. The most similar highest
priority (MSHP) principle is used for matching of textural features and Canberra distance is utilised for shape features
matching. The retrieved image is the image which has less MSHP and Canberra distance from the query image. The proposed
method is evaluated on three different image sets, which contain about 17 000 images. The experimental results indicate that
the proposed method achieves higher retrieval accuracy than several previously presented schemes, whereas the computational
complexity and processing time of the new method are less than those of other approaches.
请帮忙用专业术语翻译，不要用软件翻译来回答，谢谢！

Answer 1

利用机器学习和统计相似性匹配技术的图像检索框架
摘要：本研究的目的是利用形状和图像的纹理特性来提高性能
图像检索算法。此外，用于分割图像分成不重叠的不同尺寸的瓷砖框架，
这导致较高的检索效率，提出了。在新方法中，图像被划分为不同的区域（瓦）。
然后，能量和Hartley变换系数的标准偏差每瓦，作为局部描述符
纹理，特征提取子。接下来，边缘图像的不变矩的形状特征是用来记录。形状
特征和纹理特征的组合子提供了一个强大的功能集的图像检索。最相似的最高
优先级（MSHP）原理应用于纹理特征和堪培拉距离匹配是利用形状特征
匹配。检索到的图像是从查询图像不MSHP和堪培拉的距离图像。所提出的
方法对三个不同的图像集进行评价，其中含有约17的000幅图像。实验结果表明，
该方法实现比以前提出的方案更高的检索精度，而计算
复杂度的新方法和处理时间小于其他方法。