The Complete Guide Basics Image Classification with Deep Learning

in this tutorial, I will show The Complete Guide Basics Image Image Classification with Deep Learning. Basics Image Classification with Image Classification with Deep Learning is a series tutorial so you have to need to see all tutorials for a complete guide.

Image classification is the task of selecting a label to an image from a predefined set of categories. This means that our task is to examine an input image and return a label that categorizes the image. The label is ever from a predefined set of possible categories.

For example, let’s assume that our set of possible categories includes: categories = {cat, dog, panda} Then we present the following image to our classification system:

dog Our aim here is to take this input image and select a label to it from our categories set – in this case, dog. classification system could also assign multiple labels to the image via probabilities, such as dog: 95%; cat: 4%; panda: 1%. and given our input image of W ×H pixels with three channels, Red, Green, and Blue, respectively, our goal is to take the W ×H ×3 = N pixel image and figure out how to accurately classify the contents of the image.

When working machine learning and deep learning, we have a dataset we are trying to extract knowledge from. Each sample/item in the dataset (whether it be image data, text data, audio data, etc.) is a data point.

A dataset is for a collection of data points. Our aim is to apply machine learning and deep learning algorithms to identify underlying patterns in the dataset, enabling us to correctly analyze data points that our algorithm has not found yet. Take the time now to familiarize yourself with this technology:

1. In the setting of image classification, our dataset is a collection of images.

2. Each image is, therefore, a data point.

Take a look at the two photos. What is the difference between the two photos – there is clearly a cat on the left and a dog on the right. But the computer did not see this computer sees are two matrices of pixels.

Now we come to the problem of the semantic gap. The semantic gap is the difference between how a human sees the contents of an image versus how an image can be represented in a way a computer can understand the process.

Again, a quick observed examination of the two photos above can tell the difference between the two species of an animal. But in fact, the computer has no idea there are animals in the image, to begin with. To make this point clear, take a look at image containing a photo of a tranquil beach

.We might explain the image as follows:

Spatial: in the sky is at the top of the image and the sand are at the ground.

Color: The sky is dark blue, the sea water is a lighter blue than the sky, while the sand is tan.

Texture: The sky has a nearly uniform pattern, while the sand is very common.

How do we go about encoding all this data in a way that a computer can read it? The answer is to apply feature family to quantify the contents of an image.

Feature descent is the process of taking an input image, applying an algorithm, and taking a column vector that quantifies our image. To perform this process, we may consider applying hand-engineered features such as HOG, LBPs, or other “traditional” approaches to image quantifying.

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