S. S. Tsukhlo (Bauman Moscow State Technical University) :
Articles:
519.7 Modeling and efficiency analysis of perceptual hash functions for segmented image search
Valishin A. A. (Bauman Moscow State Technical University), Zaprivoda A. V. (Bauman Moscow State Technical University), Tsukhlo S. S. (Bauman Moscow State Technical University)
doi: 10.18698/2309-3684-2024-2-4667
This research paper explores the use of perceptual hash functions to improve the retrieval efficiency of aerial photography and satellite remote sensing images segmented by a convolutional neural network. This paper describes three hashing algorithms. The first algorithm is based on the use of a low-pass filter and is aimed at reducing image detail in order to highlight the most stable image features. The second algorithm uses a two-dimensional discrete cosine transform to create an image hash. The third algorithm is based on the Radon transform, which allows you to extract information about the directions of lines in the image, as well as provide maximum invariance to the rotation transformation of the input image. The article also tests these algorithms, including analysis of their invariance to transformations for rotation, scaling and shifting of the source image. Test results show that the algorithm based on the Radon transform exhibits good rotation invariance, but is sensitive to the quality of segmentation, which can lead to frequent collisions when searching for similar images. Algorithms using a two-dimensional discrete cosine transform and an algorithm using a low-pass filter turned out to be more stable and have a smaller spread of values. However, it should be noted that algorithms using a low-pass filter and 2D discrete cosine transform may not be applicable to rotated images. Based on the results of analysis and comparison of the performance of the algorithms, it is recommended to give preference to either the second or third algorithm, because each of them has its own advantages and disadvantages, and the decision to use a specific algorithm in the task of finding the most similar image must take into account the specific conditions and limitations of the problem, as well as the requirements for the quality of image comparison.
Валишин А.А., Запривода А.В., Цухло С.С. Моделирование и сравнительный анализ эффективности перцептивных хеш-функций для поиска сегментированных изображений. Математическое моделирование и численные методы, 2024, № 2, с. 46-67.