For humans, describing what we see with our eyes—the "visual world"—seems trivial, so much so that we don't realize it's something we do constantly. When you look at an object, whether it's a car, a big tree, or a person, you usually just say its name without thinking too much. However, for a computer, distinguishing between "human objects" and "non-human objects" like puppies, chairs, or alarm clocks is quite challenging.
Solving this problem can bring significant benefits. Image recognition technology, more broadly known as computer vision, serves as the foundation for many emerging technologies. From self-driving cars and facial recognition software to simpler yet essential innovations—such as smart factories that monitor pipeline defects or insurance companies using automation to process and classify claim photos—these advancements rely heavily on image recognition.
In the following sections, we will explore the challenges of image recognition and examine how scientists use a special type of neural network to address them.
Learning to "see" is a complex and costly task. One approach to solving this is by applying metadata to unstructured data. In previous articles, we discussed the difficulties of text classification and search in the absence of sufficient metadata. Manually classifying movies or music is already a tough job for people, but some tasks are not only laborious but also nearly impossible. For example, training a driverless car’s navigation system to distinguish between vehicles and pedestrians, or labeling and categorizing thousands of user-uploaded images and videos on social media daily.
The only viable solution is to use neural networks. While theoretically possible, conventional neural networks are computationally expensive when applied to image analysis. Even processing a small 30x30 pixel image requires 900 input values and over 500,000 parameters. This becomes impractical when dealing with larger images, such as 500x500 pixels, where the number of inputs and parameters increases dramatically, making it computationally infeasible.
Additionally, using neural networks for image recognition may lead to overfitting. Overfitting occurs when the model becomes too closely tailored to the training data, resulting in poor performance on new, unseen data. This reduces the model’s ability to generalize and can significantly impact its accuracy.
The real solution lies in convolution. Fortunately, a slight modification to the structure of neural networks makes image processing more efficient. This modified version is called a "convolutional neural network," or CNN. Unlike traditional neural networks, CNNs are specifically designed for image processing, sacrificing some of their general adaptability to achieve better performance in this domain.
In any image, nearby pixels are more related than distant ones. Convolutional neural networks take advantage of this principle. Traditional networks connect each pixel to a separate neuron, increasing computational load and reducing accuracy. CNNs reduce unnecessary connections by focusing on local regions, mimicking how the human brain processes visual information—each neuron responds to a small part of the overall field.
The inner secret of convolutional neural networks lies in two key layers: the convolutional layer and the pooling layer. Let's walk through a practical example: determining if a photo contains a "grandmother." The first step is the convolutional layer, which breaks the image into overlapping 3x3 pixel tiles. Each tile is processed by a simple neural network, transforming it into a set of feature maps. These maps represent different parts of the image in a three-dimensional numerical format.
Next comes the pooling layer, which reduces the spatial dimensions of the data while retaining the most important features. This helps minimize computation and prevents overfitting. Finally, the processed data is passed to a fully connected neural network, which produces the final output—how confident the system is in identifying a grandmother in the photo.
This is just a simplified explanation. In reality, CNNs are far more complex, often involving hundreds or even thousands of layers. Implementing a convolutional neural network is time-consuming and resource-intensive, but several APIs now make it easier for organizations to perform image analysis without needing in-house experts.
Google Cloud Vision, for instance, uses the TensorFlow framework and offers a wide range of features, including object detection, face recognition, OCR, and image search. IBM Watson Visual Recognition allows customization based on provided images and includes similar capabilities. Clarif.ai is another rising service that provides customizable image recognition solutions for specific use cases like weddings or food.
While these APIs are great for common applications, specialized tasks may still require custom solutions. Fortunately, many deep learning frameworks, such as TensorFlow, Deep Learning4J, and Theano, help reduce the burden on developers, allowing them to focus more on model training and less on infrastructure.
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