Notice that I am not prescribing what pre-processing steps are good. The reason is that nobody knows in advance which of these preprocessing steps will produce good results. You try a few different ones and some might give slightly better results. Here is a paragraph from Dalal and Triggs. These normalizations have only a modest effect on performance, perhaps because the subsequent descriptor normalization achieves similar results. We do use colour information when available.
RGB and LAB colour spaces give comparable results, but restricting to grayscale reduces performance by 1. As you can see, they did not know in advance what pre-processing to use. They made reasonable guesses and used trial and error. As part of pre-processing, an input image or patch of an image is also cropped and resized to a fixed size. This is essential because the next step, feature extraction, is performed on a fixed sized image.
The input image has too much extra information that is not necessary for classification. Therefore, the first step in image classification is to simplify the image by extracting the important information contained in the image and leaving out the rest. For example, if you want to find shirt and coat buttons in images, you will notice a significant variation in RGB pixel values. However, by running an edge detector on an image we can simplify the image. You can still easily discern the circular shape of the buttons in these edge images and so we can conclude that edge detection retains the essential information while throwing away non-essential information.
The step is called feature extraction. In traditional computer vision approaches designing these features are crucial to the performance of the algorithm.
Turns out we can do much better than simple edge detection and find features that are much more reliable. In our example of shirt and coat buttons, a good feature detector will not only capture the circular shape of the buttons but also information about how buttons are different from other circular objects like car tires. A feature extraction algorithm converts an image of fixed size to a feature vector of fixed size. HOG is based on the idea that local object appearance can be effectively described by the distribution histogram of edge directions oriented gradients.
Using the gradient images and , we can calculate the magnitude and orientation of the gradient using the following equations. Histogram of these gradients will provide a more useful and compact representation. We will next convert these numbers into a 9-bin histogram i. The bins of the histogram correspond to gradients directions 0, 20, 40 … degrees. Every pixel votes for either one or two bins in the histogram. If the direction of the gradient at a pixel is exactly 0, 20, 40 … or degrees, a vote equal to the magnitude of the gradient is cast by the pixel into the bin.
A pixel where the direction of the gradient is not exactly 0, 20, 40 … degrees splits its vote among the two nearest bins based on the distance from the bin. A pixel where the magnitude of the gradient is 2 and the angle is 20 degrees will vote for the second bin with value 2. On the other hand, a pixel with gradient 2 and angle 30 will vote 1 for both the second bin corresponding to angle 20 and the third bin corresponding to angle Block normalization : The histogram calculated in the previous step is not very robust to lighting changes.
Multiplying image intensities by a constant factor scales the histogram bin values as well. To counter these effects we can normalize the histogram — i.
The idea is the same, but now instead of a 9 element vector you have a 36 element vector. What is the length of the final vector?
Step 3 : Learning Algorithm For Classification In the previous section, we learned how to convert an image to a feature vector. In this section, we will learn how a classification algorithm takes this feature vector as input and outputs a class label e.steermaipleasleephi.gq
Scalable logo recognition in real-world images
Before a classification algorithm can do its magic, we need to train it by showing thousands of examples of cats and backgrounds. Although the ideas used in SVM have been around since , the current version was proposed in by Cortes and Vapnik. In the previous step, we learned that the HOG descriptor of an image is a feature vector of length We can think of this vector as a point in a dimensional space.
Visualizing higher dimensional space is impossible, so let us simplify things a bit and imagine the feature vector was just two dimensional. In our simplified world, we now have 2D points representing the two classes e. In the image above, the two classes are represented by two different kinds of dots.
All black dots belong to one class and the white dots belong to the other class. During training, we provide the algorithm with many examples from the two classes. In other words, we tell the algorithm the coordinates of the 2D dots and also whether the dot is black or white.
Different learning algorithms figure out how to separate these two classes in different ways. Linear SVM tries to find the best line that separates the two classes. In the figure above, H1, H2, and H3 are three lines in this 2D space. H1 does not separate the two classes and is therefore not a good classifier.
H2 and H3 both separate the two classes, but intuitively it feels like H3 is a better classifier than H2 because H3 appears to separate the two classes more cleanly. Because H2 is too close to some of the black and white dots. China was not beating up ed examinations now at the loss, further rising the so-called stadium.
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Jingying Chen (Author of LOGO Recognition)
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