Difference between Low-Level and High-Level Feature Detection/ Extraction
Asked Answered
D

4

5

According to this Wikipedia article Feature Extraction examples for Low-Level algorithms are Edge Detection, Corner Detection etc.

But what are High-Level algorithms?

I only found this quote from the Wikipedia article Feature Detection (computer vision):

Occasionally, when feature detection is computationally expensive and there are time constraints, a higher level algorithm may be used to guide the feature detection stage, so that only certain parts of the image are searched for features.

Could you give an example of one of these higher level algorithms?

Dropsonde answered 27/10, 2014 at 15:12 Comment(0)
T
11

There isn't a clear cut definition out there, but my understanding of "high-level" algorithms are more in tune with how we classify objects in real life. For low-level feature detection algorithms, these are mostly concerned with finding corresponding points between images, or finding those things that classify as something even remotely interesting at the lowest possible level you can think of - things like finding edges or lines in an image (in addition to finding interesting points of course). In addition, anything dealing with pixel intensities or colours directly is what I would consider low-level too.

High-level algorithms are mostly in the machine learning domain. These algorithms are concerned with the interpretation or classification of a scene as a whole. Things like body pose classification, face detection, classification of human actions, object detection and recognition and so on. These algorithms are concerned with training a system to recognize or classify something, then you provide it some unknown input that it has never seen before and its job is to either determine what is happening in the scene, or locate a region of interest where it detects an action that the system is trained to look for. This latter fact is probably what the Wikipedia article is referring to. You would have some sort of pre-processing stage where you have some high-level system that determines salient areas in the scene where something important is happening. You would then apply low-level feature detection algorithms in this localized area.

There is a great high-level computer vision workshop that talks about all of this, and you can find the slides and code examples here: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/teaching/courses/ss-2019-high-level-computer-vision/

Good luck!

Toon answered 27/10, 2014 at 22:37 Comment(0)
B
3

High-level features are something that we can directly see and recognize, like object classification, recognition, segmentation and so on. These are usually the goal of CV research, which is always based on 'low-level' features and algorithms.

Blades answered 28/2, 2016 at 14:7 Comment(0)
S
0

Two of them are used in machine specially x-ray machine Concerned Scene as a whole and edges of lines to help soft ware of machine to take good decision.

Shaeffer answered 21/1, 2022 at 8:36 Comment(0)
S
-1

I think we should not confuse with high-level features and high-level inference. To me, high-level features mean shape, size, or a combination of low-level features etc. are the high-level features. While classification is the decision made based on the high-level features.

Seaquake answered 11/9, 2021 at 8:22 Comment(2)
is this meant as a comment?Maurili
This does not provide an answer to the question. Once you have sufficient reputation you will be able to comment on any post; instead, provide answers that don't require clarification from the asker. - From ReviewMcdougal

© 2022 - 2024 — McMap. All rights reserved.