Computer vision is a subject to convert images and videos into machine-understandable signals. With these signals, programmers can further control the behavior of the machine based on this high-level understanding. Among many computer vision tasks, image classification is one of the most fundamental ones. It not only can be used in lots of real products like Google Photo’s tagging and AI content moderation but also opens a door for lots of more advanced vision tasks, such as object detection and video understanding. Due to the rapid changes in this field since the breakthrough of Deep Learning, beginners often find it too overwhelming to learn. Unlike typical software engineering subjects, there are not many great books about image classification using DCNN, and the best way to understand this field is though reading academic papers. But what papers to read? Where do I start? In this article, I’m going to introduce 10 best papers for beginners to read. With these papers, we can see how this field evolve, and how researchers brought up new ideas based on previous research outcome. Nevertheless, it is still helpful for you to sort out the big picture even if you have already worked in this area for a while. So, let’s get started.
Gradient-based Learning Applied to Document Recognition
Introduced in 1998, LeNet sets a foundation for future image classification research using the Convolution Neural Network. Many classical CNN techniques, such as pooling layers, fully connected layers, padding, and activation layers are used to extract features and make a classification. With a Mean Square Error loss function and 20 epochs of training, this network can achieve 99.05% accuracy on the MNIST test set. Even after 20 years, many state-of-the-art classification networks still follows this pattern in general.
ImageNet Classification with Deep Convolutional Neural Networks
Although LeNet achieved a great result and showed the potential of CNN, the development in this area stagnated for a decade due to limited computing power and the amount of the data. It looked like CNN can only solve some easy tasks such as digit recognition, but for more complex features like faces and objects, a HarrCascade or SIFT feature extractor with an SVM classifier was a more preferred approach.
However, in 2012 ImageNet Large Scale Visual Recognition Challenge, Alex Krizhevsky proposed a CNN-based solution for this challenge and drastically increased ImageNet test set top-5 accuracy from 73.8% to 84.7%. Their approach inherits the multi-layer CNN idea from LeNet, but increased the size of CNN a lot. As you can see from the diagram above, the input is now 224x224 compared with LeNet’s 32x32, also many Convolution kernels have 192 channels compared with LeNet’s 6. Although the design isn’t changed much, with hundreds of more times of parameters, the network’s ability to capture and represent complex features improved hundreds of times too. To train such as a big model, Alex used two GTX 580 GPU with 3GB RAM for each, which pioneered a trend of GPU training. Also, the use of ReLU non-linearity also helped to reduce computation cost.
In addition to bringing many more parameters for the network, it also explored the overfitting issue brought by a larger network by using a Dropout layer. Its Local Response Normalization method didn’t get too much popularity afterward but inspired other important normalization techniques such as BatchNorm to combat with gradient saturation issue. To sum up, AlexNet defined the de facto classification network framework for the next 10 years: a combination of Convolution, ReLu non-linear activation, MaxPooling, and Dense layer.
Very Deep Convolutional Networks for Large-Scale Image Recognition
With such a great success of using CNN for visual recognition, the entire research community blew up and all started to look into why this neural network works so well. For example, in “Visualizing and Understanding Convolutional Networks” from 2013, Matthew Zeiler discussed how CNN pick up features and visualized the intermediate representations. And suddenly everyone started to realize that CNN is the future of computer vision since 2014. Among all those immediate followers, the VGG network from Visual Geometry Group is the most eye-catching one. It got a remarkable result of 93.2% top-5 accuracy, and 76.3% top-1 accuracy on the ImageNet test set.
Following AlexNet’s design, the VGG network has two major updates: 1) VGG not only used a wider network like AlexNet but also deeper. VGG-19 has 19 convolution layers, compared with 5 from AlexNet. 2) VGG also demonstrated that a few small 3x3 convolution filters can replace a single 7x7 or even 11x11 filters from AlexNet, achieve better performance while reducing the computation cost. Because of this elegant design, VGG also became the back-bone network of many pioneering networks in other computer vision tasks, such as FCN for semantic segmentation, and Faster R-CNN for object detection.
With a deeper network, gradient vanishing from multi-layers back-propagation becomes a bigger problem. To deal with it, VGG also discussed the importance of pre-training and weight initialization. This problem limits researchers to keep adding more layers, otherwise, the network will be really hard to converge. But we will see a better solution for this after two years.
Going Deeper with Convolutions
VGG has a good looking and easy-to-understand structure, but its performance isn’t the best among all the finalists in ImageNet 2014 competitions. GoogLeNet, aka InceptionV1, won the final prize. Just like VGG, one of the main contributions of GoogLeNet is to push the limit of the network depth with a 22 layers structure. This demonstrated again that going deeper and wider is indeed the right direction to improve accuracy.
Unlike VGG, GoogLeNet tried to address the computation and gradient diminishing issues head-on, instead of proposing a workaround with better pre-trained schema and weights initialization.
First, it explored the idea of asymmetric network design by using a module called Inception (see diagram above). Ideally, they would like to pursuit sparse convolution or dense layers to improve feature efficiency, but modern hardware design wasn’t tailored to this case. So they believed that a sparsity at the network topology level could also help the fusion of features while leveraging existing hardware capabilities.
Second, it attacks the high computation cost problem by borrowing an idea from a paper called “Network in Network”. Basically, a 1x1 convolution filter is introduced to reduce dimensions of features before going through heavy computing operation like a 5x5 convolution kernel. This structure is called “Bottleneck” later and widely used in many following networks. Similar to “Network in Network”, it also used an average pooling layer to replace the final fully connected layer to further reduce cost.
Third, to help gradients to flow to deeper layers, GoogLeNet also used supervision on some intermediate layer outputs or auxiliary output. This design isn’t quite popular later in the image classification network because of the complexity, but getting more popular in other areas of computer vision such as Hourglass network in pose estimation.
As a follow-up, this Google team wrote more papers for this Inception series. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift” stands for InceptionV2. “Rethinking the Inception Architecture for Computer Vision” in 2015 stands for InceptionV3. And “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning” in 2015 stands for InceptionV4. Each paper added more improvement over the original Inception network and achieved a better result.
#computer-vision #deep-learning #machine-learning #artificial-intelligence #deep learning
During my studies at JKU there was a task for preprocessing images for a machine learning project. It is necessary to clean the raw images before using them in a learning algorithm, so thats why we create a pre-processing function. I think it can be quite useful for others as well so I want to share a bit of my approach. The file is structured in a way that it is easy to understand and also should have a tutorial-like effect.
#image-recognition #image #image-classification #machine-learning #image-processing
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In this image validation in laravel 7/6, i will share with you how validate image and image file mime type like like jpeg, png, bmp, gif, svg, or webp before uploading image into database and server folder in laravel app.
#laravel image validation #image validation in laravel 7 #laravel image size validation #laravel image upload #laravel image validation max #laravel 6 image validation
Welcome to my Blog, in this article we learn about how to integrate CKEditor in Django and inside this, we enable the image upload button to add an image in the blog from local. When I add a CKEditor first time in my project then it was very difficult for me but now I can easily implement it in my project so you can learn and implement CKEditor in your project easily.
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Processing an image in order to derive some meaningful information from the image is known as image processing. It can be called a scientific study where we apply different methods or functions on images to find out what are its different features. We can enhance the image or degrade the image in order to extract unique features.
Mahotas is a computer vision and image processing library for python. It is implemented using C++ so it is fast and it operates over NumPy arrays. Currently, it has around 100 functions for computer vision and image processing.and is ever-growing.
In this article, we will explore what are the different functions and methods that are there in Mahotas which can be used for image processing.
Like any other python library, we can install mahotas using pip install mahotas.
We will import all the functionalities of mahotas and other than that we will import pylab for image display functions.
from mahotas import *
from pylab import imshow, show
We can use any image for image processing. I am using a bird image that I downloaded from google. We will use mahotas to load the image.
img = mahotas.imread('/bird.jpg')
Now we will perform different operations using mahotas and find out the important features and information about the image we are using.
#developers corner #complete guide #image analysis #image classification #image processing #image recognition #mahotas #python programming