Image classification and object detection techniques are important methods when it comes to working on the domain of computer vision. These techniques help machines understand and identify real-time objects and environments with the help of digital images as inputs. Over the years, computer vision techniques have been used in several sectors, including healthcare, manufacturing, retail, to name a few.
Since the methods like image classification and object detection revolve around identifying objects in digital images, often it leaves confusion: what these two techniques actually are and how these two techniques differ from each other?
In this article, we will understand what these two techniques are and discuss the various scenarios that make them different.
In simple words, image classification is a technique that is used to classify or predict the class of a specific object in an image. The main goal of this technique is to accurately identify the features in an image.
How Image Classification Works
In general, the image classification techniques can be categorised as parametric and non-parametric or supervised and unsupervised as well as hard and soft classifiers. For supervised classification, this technique delivers results based on the decision boundary created, which mostly rely on the input and output provided while training the model. But, in the case of unsupervised classification, the technique provides the result based on the analysis of the input dataset own its own; features are not directly fed to the models.
The main steps involved in image classification techniques are determining a suitable classification system, feature extraction, selecting good training samples, image pre-processing and selection of appropriate classification method, post-classification processing, and finally assessing the overall accuracy. In this technique, the inputs are usually an image of a specific object, such as the rabbit in the above picture, and the outputs are the predicted classes that define and match the input objects. Convolutional Neural Networks (CNNs) is the most popular neural network model that is used for image classification problem.
Types of Image Classification Techniques
The supervised image classification techniques include parallelepiped technique, minimum distance classifier, maximum likelihood classifier, among others. In a research paper, the researchers mentioned several types of image classification techniques as mentioned below
- Image classification based on the information acquired from different sensors
- Image classification based on the nature of the training sample used in classification
- Image classification based on the basis of the various parameter used on data
- Image classification based on the nature of pixel information used on data
- Image classification based upon the number of outputs generated for each spatial data element
- Image classification based upon the nature of spatial information.
- In supervised and unsupervised image classification techniques, the disadvantages are the extensive amount of time required during the training phase and are not suitable to deal with big data.
The problem definition of object detection is to determine where objects are located in a given image such as object localisation and which category each object belongs to, i.e. object classification. In simple words, object detection is a type of image classification technique, and besides classifying, this technique also identifies the location of the object instances from a large number of predefined categories in natural images.
This technique has the capability to search for a specific class of objects, such as cars, people, animals, birds, etc. and has successfully been used in the next-generation image as well as video processing systems. The recent advancements in this technique have only become possible with the advent of deep learning methodologies.
Object detection techniques can be used in real-world projects such as face detection, pedestrian detection, vehicle detection, traffic sign detection, video surveillance, among others.
How Object Detection Works
The pipeline of traditional object detection models can be mainly divided into three stages, that are informative region selection, feature extraction and classification. There are several popular deep learning-based models for object detection, which have been used by organisations and academia to achieve efficiency as well as accurate results in detecting objects from images. The popular models include MobileNet, You Only Live Once (YOLO), Mark-RCNN, RetinaNet, among others.
Over the past few years, great success has been achieved in a controlled environment for object detection problem. However, the problem remains unsolved in uncontrolled places, in particular, when objects are placed in arbitrary poses in a cluttered and occluded environment.
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