We will tackle in this article how to train image recognition models. Also, let us learn things about image recognition models.
Train Image Recognition Models
What are image recognition models all about? Image recognition models are all about using neural networks for visual recognition tasks.
So the Neural network is used for image recognition tasks. It is because it can learn to recognize patterns as well as images as it is trained on large datasets.
For example, the ImageNet dataset consists of more than 14 million images. Furthermore, the Color co-occurrence matrix (CCM) is mainly used for image recognition with neural nets.
It contains the frequency with which colors co-occur in images. The CCM is a 2D array and each element of this array consists of a value.
This is indicating the number of times two colors co-occurred in a given image.
Train Image Recognition Models
The most common way to train CCM is by using the HSV color space. For example, if we have an input image consisting of three objects (object1, object2 & object3).
Then, the number of pixels in each object is 600 × 600. So we can create a CCM by using the following code:
- The first object will have the color attributes (red, green & blue) as (100, 100 & 100).
- The second object will have the color attributes as (100, 33 & 100).
- The third object will have the color attributes as (0, 0 & 100).
So, our first object will have several occurrences as 400. It is because 400 pixels are red. Then, our first object will have 400/600 = 0.67 occurrences per pixel.
Similarly, our second object will have several occurrences as 200. It is because 200 pixels are green. So, our second object will have 200/400 = 0.50 occurrences per pixel.
Finally, our third object will have several occurrences as 0. It is because there are no blue pixels in it. So, our third object will have 0/0 = 0 occurrences per pixel.
Image Recognition Models Work
How do these image recognition models work? The image recognition models are mainly categorized into two types.
They are as follows:
1. Bottom-up approach
2. Top-down approach
Let us tackle these two types in detail way from the following:
Bottom-up approach
In this approach, the image is first decomposed into its basic parts. Which are known as feature detectors.
After that, these basic features will be further broken down into smaller parts. That is subsequently used to generate the final representation.
The final representation is known as the output of an image recognition model. So, to start with this process.
We need to choose what kind of feature detectors will be used for decomposition. The main purpose of using the bottom-up approach.
It is capable of extracting higher-level features from raw data than the top-down approach. It is because it will use raw data directly.
Without any preprocessing involved. However, there are some drawbacks associated with this approach.
It is because this approach requires a vast amount of knowledge about the given problem domain. Also requires expertise in machine learning algorithms.
It can be used for advanced problems but not for basic ones. On the other hand, the top-down approach uses the basic features.
It is to create high-level ones and then use these high-level features to create an output.
Top-down approach
This approach makes use of high-level features initially. Then generates low-level features from them before returning an output.
It requires less knowledge about the problem domain than the bottom-up approach. But it requires expertise in machine learning algorithms.
As well as knowledge about how these top-level features should be generated.
