Why Deep Learning is Critical for Image Recognition?
The field of Artificial Intelligence is gaining widespread popularity & its applications have become more prevalent in everyday life & in the industries across the verticals. Being multidisciplinary in nature there are numerous applications of Artificial Intelligence & one of the most prominent applications of Artificial Intelligence is Deep Learning for Image Recognition. The smart face unlock feature in our smartphones is an application of Image Recognition.
What Exactly is Image Recognition?
Image Recognition comes under the category of Computer Vision technology. Let’s give you a clear idea about how Image Recognition is different from other applications of Computer Vision
a) Image Detection is the process where machines would be capable of detecting certain objects in an image. Today’s Image Detection is at a level where machines would be capable of detecting multiple objects in an image by drawing bounding boxes around them.
b) Image Classification is the process where machines would be capable of detecting and classifying objects in an image in accordance to their category, for example, cat, dog, etc.
c) Image Recognition is a complex technique that involves both Image Detection and Image classification technique. As a part of the Image Recognition technique machines will be trained to detect, classify, and identify the objects by matching them with given data. The face unlock feature in our smartphones works on the same concept. The facial unlock system in our smartphones authenticates a human face by matching it with database input.
Inside a Deep Learning Model for Image Recognition:
As we are clearly aware of the fact that Deep Learning models are made up of several neural networks whose functioning is similar to that of the functioning of neurons inside a human brain. In the process of Object Localization and Image Recognition, the system makes use of Region-based Convolutional Neural Networks or R-CNNs that are also a part of Deep Neural Networks.
A typical R-CNN model comprises the following three modules
a) Region Proposal that helps in the creation of candidate bounding boxes
b) Feature Extractor that helps in the process of extracting features from the identified objects
c) Classifier for annotating the object with labels or categories.
The benefit of using R-CNN approach instead of CNN is the faster and more accurate responses. To make precisions conclusions regarding the classification of the object, R-CNN model needs to be trained with relevant images and visual information.
The most important parameters while training a neural network model include-
a) Size, quality, and quantity of images
b) Number of color channels
c) Aspect ratio and image scaling
d) The mean and standard deviation of input data
e) Available data variations, and more
Smart Applications of Image Recognition:
Some of the smart applications of Image Recognition include
- Deep Video Analytics
- Medical Image Analysis
- Automated Image Caption Generator
Image recognition can be done by using either CNN models or R-CNN & comparatively, R-CNN model in Deep Learning is more accurate and prompt when it comes to recognizing images. The Deep Learning R-CNN model needs to be trained using images and visual information & the extent of accuracy in the Deep Learning powered Image Recognition model relies heavily on quality of training data.
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