# Classification, Object Detection and Image Segmentation
Source: [https://docs.qualcomm.com/doc/80-63442-4/topic/classification-object-detection-and-image-segmentation.html](https://docs.qualcomm.com/doc/80-63442-4/topic/classification-object-detection-and-image-segmentation.html)
Attaining deeper understanding of images for machine learning models
Some computer vision models can detect objects, determine their shape and predict the
direction in which they will travel. For example, such models are at work in
self-driving cars. Three important tasks undertaken by computer vision are
**classification**, **object detection** and **image segmentation**.
## Classification
Classification is a machine learning task for determining which objects are in an
image or video. It refers to training machine learning models with the intent of
finding out which classes (objects) are present. Classification is useful at the
yes-no level of deciding whether an image contains an object/anomaly or not.
A separate task from classification is localization, or determining the position of
the classified objects in the image or video.
## Object detection
Object detection combines classification and localization to determine what objects
are in the image or video and specify where they are in the image. It applies
classification to distinct objects and uses bounding boxes, as shown below.
Object detection is useful in identifying objects in an image or video. Below, the
image on the left illustrates classification, in which the classes *Donut* and
*Coffee* are identified. The image on the right illustrates object
detection by surrounding the members of each class — donut and coffee — with a
bounding box.
Use cases for object detection include facial detection with any post-detection
analysis; for example, expression detection, age estimation or drowsiness detection.
Many real-time object detection applications exist for traffic management, such as
vehicle detection systems based on traffic scenes.
As described above, the most popular approaches to computer vision are classification
and object detection to identify objects present in an image and specify their
position. But many use cases call for analyzing images at a lower level than that.
That is where image segmentation comes in.
## Image segmentation
Any image consists of both useful and useless information, depending on the user’s
interest. Image segmentation separates an image into regions, each with its
particular shape and border, delineating potentially meaningful areas for further
processing, like classification and object detection. The regions may not take up
the entire image, but the goal of image segmentation is to highlight foreground
elements and make it easier to evaluate them. Image segmentation provides
pixel-by-pixel details of an object, making it different from classification and
object detection.
Below, the image on the left illustrates object detection, highlighting only the
location of the objects. The image on the right illustrates image segmentation,
showing pixel-by-pixel outlines of the objects.
## Image segmentation algorithms
Image segmentation techniques use different algorithms.
| Algorithm | Description |
| --- | --- |
| Edge Detection Segmentation | Makes use of discontinuous local features of an image to detect
edges and hence define a boundary of the object. |
| Mask R-CNN | Gives three outputs for each object in the image: its class,
bounding box coordinates, and object mask |
| Segmentation based on Clustering | Divides the pixels of the image into homogeneous
clusters. |
| Region-Based Segmentation | Separates the objects into different regions based on threshold
value(s). |
To solve segmentation problems in a given domain, it is usually necessary to combine
algorithms and techniques with specific knowledge of the domain.
(For a comprehensive look at image segmentation, read [Image Segmentation Algorithms Overview](https://arxiv.org/ftp/arxiv/papers/1707/1707.02051.pdf) by Song Yuheng
and Yan Hao.)
**Parent Topic:** [Image Segmentation using DeepLab-v3 and Qualcomm Neural Processing SDK for AI](https://docs.qualcomm.com/doc/80-63442-4/topic/image-segmentation-deeplabv3.html)
Last Published: Jun 24, 2024
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