WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that WiMi is working on feature transformation technique for image data augmentation, which is a commonly used method in image data augmentation to increase the diversity and richness of an image by performing a series of feature transformation operations on the image, thus improving the generalization ability of machine learning algorithms. The feature transformation can generate a new image by changing the color, shape, texture and other features of the image, so that the model can be better adapted to different scenes and objects. In practical applications, different feature transformation techniques can be selected and combined according to specific needs to achieve the best effect.
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A common feature transformation is image rotation. By performing a image rotation, the angle and orientation of the image can be changed, thus increasing the diversity of the image. For example, when training a target detection model, the image can be randomly rotated by a certain angle, enabling the model to better adapt to targets at different angles. And another common feature transformation technique is image panning. By performing a panning operation on an image, the position and layout of the image can be changed, thus increasing the diversity of the image. For example, when training an image classification model, the image can be randomly translated by a certain distance, enabling the model to better adapt to objects at different locations. In addition to rotation and panning, there are many other feature transformation techniques that can be used for image data augmentation, such as scaling, flipping, and clipping. These techniques can be selected and combined according to specific application scenarios and needs to achieve the best results.
This technique applied in image data augmentation can increase the data samples. For example, by performing feature transformation operations such as rotating, flipping, scaling, and panning on the original image, multiple new image samples can be generated, thereby expanding the size of the training dataset and improving the generalization ability of the model. By increasing the diversity of data, the model is thus better adapted to various noise and missing situations. In addition, the generalization ability of the model can be further improved by applying multiple feature transformation techniques in combination. Through the two feature transformation techniques, rotation transformation and scale transformation, the model can be exposed to more images at different angles and scales during the training process, thus improving its adaptability to rotation and scale transformation, and thus enhancing the performance of the model in practical applications.
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The feature transformation technique researched by WiMi for image data augmentation include brightness adjustment, color transformation, geometric transformation, noise addition and so on. Brightness adjustment include histogram equalization, contrast stretching, and adaptive histogram equalization, which can make the details of the image clearer and enhance the visual effect of the image. By changing the color space of the image, the color and tone of the image can be changed. Color transformation include RGB to grayscale conversion, RGB to HSV conversion and RGB to LAB conversion, etc. These methods can make the colors of the image more vivid and increase the visual impact of the image. Geometric transformation refers to changing the shape and structure of an image by performing geometric transformations such as translation, rotation, scaling and flipping to make the shape of the image more diverse and increase the visual variability of the image. Noise addition refers to adding noise to the image to simulate the noise situation in the real scene, thus increasing the complexity of the image, making the image more realistic and enhancing the visual realism of the image.
By comprehensively applying the above feature transformation techniques of WiMi, a large number of image samples can be generated, thus expanding the image dataset and improving the generalization ability of the machine learning algorithm. In practical applications, we can also choose appropriate feature transformation techniques according to the needs of specific tasks and combine them with machine learning algorithms for training and testing.
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