Image classsification and object detection techniques are important methods used in computer vision.

Image classsification and object detection techniques are important methods used in computer vision. These techniques help machines understand and identify real-time objects and environments with the help of digital images as inputs. Since these two methods turn around identifying objects in digital images, it often leaves confusion: what image classsification and object detection techniques actually are and how they differ from each other?

In this article, we will learn about these two techniques and discuss what makes them different.

Image classification

In simple terms, 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.

Below you may see several types of image classification techniques:

• 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.

The extensive amount of time required during the training phase and big data are the main drawbacks of image classification technique.

Object detection

The aim 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. Object detection techniques can be used in real-world projects such as face detection, pedestrian detection, vehicle detection, traffic sign detection, video surveillance, etc.

Over the past few years, great success has been achieved in a controlled environment for object detection problem. However, in uncontrolled places, in particular, when objects are placed in arbitrary poses in a cluttered and occluded environment, the problem remains unsolved.

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