Identify and recognize break in your image. Our image recognition tool uses machine learning and will also identify other objects found in your image. You can also select and vary the detection confidence and the number of objects that you want to detect.
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The word and object 'break' has a frequency score of 5.30 out of 7, which means that it is a popular word.
According to the english dictionary, some meanings of 'break' include:
- (tennis) a score consisting of winning a game when your opponent was serving (noun)
- breaking of hard tissue such as bone (noun)
- enter someone's (virtual or real) property in an unauthorized manner, usually with the intent to steal or commit a violent act (verb)
- be released or become known, of news (verb)
- fail to agree with, be in violation of, as of rules or patterns (verb)
- an act of delaying or interrupting the continuity (noun)
- lessen in force or effect (verb)
- an escape from jail (noun)
- interrupt a continued activity (verb)
- force out or release suddenly and often violently something pent up (verb)
- any frame in which a bowler fails to make a strike or spare (noun)
- go to pieces (verb)
- ruin completely (verb)
- act in disregard of laws, rules, contracts, or promises (verb)
- become fractured, break or crack on the surface only (verb)
- do a break dance (verb)
- prevent completion (verb)
- become separated into pieces or fragments (verb)
- make known to the public information that was previously known only to a few people or that was meant to be kept a secret (verb)
- terminate (verb)
- surpass in excellence (verb)
- discontinue an association or relation, go different ways (verb)
- the act of breaking something (noun)
- happen (verb)
- break down, literally or metaphorically (verb)
- a pause from doing something (as work) (noun)
- stop operating or functioning (verb)
- fracture a bone of (verb)
- reduce to bankruptcy (verb)
- assign to a lower position, reduce in rank (verb)
- (geology) a crack in the earth's crust resulting from the displacement of one side with respect to the other (noun)
- break a piece from a whole (verb)
- make submissive, obedient, or useful (verb)
- a time interval during which there is a temporary cessation of something (noun)
- move away or escape suddenly (verb)
- a personal or social separation (as between opposing factions) (noun)
- some abrupt occurrence that interrupts an ongoing activity (noun)
- destroy the completeness of a set of related items (verb)
- cease an action temporarily (verb)
- an unexpected piece of good luck (noun)
- an abrupt change in the tone or register of the voice (as at puberty or due to emotion) (noun)
- a sudden dash (noun)
- be broken in (verb)
- become punctured or penetrated (verb)
- cause the failure or ruin of (verb)
- cause to give up a habit (verb)
- change directions suddenly (verb)
- change suddenly from one tone quality or register to another (verb)
- come forth or begin from a state of latency (verb)
- come into being (verb)
- come to an end (verb)
- crack, of the male voice in puberty (verb)
- curl over and fall apart in surf or foam, of waves (verb)
- destroy the integrity of, usually by force, cause to separate into pieces or fragments (verb)
- diminish or discontinue abruptly (verb)
- emerge from the surface of a body of water (verb)
- exchange for smaller units of money (verb)
- fall sharply (verb)
- find a flaw in (verb)
- find the solution or key to (verb)
- give up (verb)
- happen or take place (verb)
- interrupt the flow of current in (verb)
- invalidate by judicial action (verb)
- make a rupture in the ranks of the enemy or one's own by quitting or fleeing (verb)
- make the opening shot that scatters the balls (verb)
- pierce or penetrate (verb)
- render inoperable or ineffective (verb)
- scatter or part (verb)
- separate from a clinch, in boxing (verb)
- the occurrence of breaking (noun)
- the opening shot that scatters the balls in billiards or pool (noun)
- undergo breaking (verb)
- vary or interrupt a uniformity or continuity (verb)
- weaken or destroy in spirit or body (verb)
What is Machine Learning?
Machine learning is a popular application of artificial intelligence where a system gets the ability to learn automatically and improve from its experiences without being programmed explicitly. It focuses on developing computer programs that have access to data and can use that data to recognize patterns and improve on their own.
The process of machine learning begins with an observation that includes some instruction or experience. This data is then followed by a pattern to make informed decisions in the future based on the guidelines provided.
The primary goal of machine learning is to train computers in a way that they can learn automatically without any human intervention and adjust their actions accordingly.
Why Machine Learning is Important
Data is an essential block of almost all businesses. Data-driven decisions make all the difference between keeping up with the competition or falling behind.
Machine learning is the key for companies to unlock the value of customer data and make enacting decisions to stay at the top of their game. It has made it possible to automatically produce quick models that can analyze big as well as complex data and deliver fast as well as accurate results even if they are on a vast scale.
Different Types of Machine Learning
Machine learning algorithms are usually classified as supervised or unsupervised:
Supervised Machine Learning Algorithms
Supervised learning refers to a class of problem that uses a model to draw mapping between input data and the target variable. In this approach, you teach machines by example. During training, the machines are exposed to a broad set of labeled data and are trained to predict the outcome of future events.
For example, Facebook recently announced that it had 3.5 billion public images available on Instagram with hashtags used as labels. These billions of photos with labels attached to them are then trained using image-recognition and yield record levels of accuracy.
Unsupervised Machine Learning Algorithms
Unsupervised algorithms are designed to group similar data or anomalies that stand out in data. Their purpose is to identify patterns and spot the similarities that can split data into different categories. One of the famous examples of an unsupervised learning algorithm is Airbnb, where you create a cluster of accommodations that are available for rent. Another example is Google News, where similar stories are grouped each day.
Semi-Supervised Machine Learning Algorithms
As the name suggests, this algorithm is the mix of supervised and unsupervised learning. This technique uses a small amount of labeled data and a large amount of unlabeled data to train machines. Firstly, the labeled data partially train a machine learning model, and then this partially trained model labels all the unlabeled data. This process is called pseudo-labeling. Due to the rise in semi-supervised algorithms, the importance of labeled data for training machine systems is decreasing over time.
Reinforcement Machine Learning Algorithms
A reinforced machine learning algorithm is based on the trial and error search. In this method, the machines interact with the environment by producing actions and discover their rewards or errors. You can understand the reinforcement algorithm by thinking of a person who is trying to play a new game but is not familiar with the rules.
Eventually, by pressing different buttons and seeing what happens on the screen, his performance gets better. The same process is used in reinforcement learning. Trial, error, and reward are the essential characteristics of this machine learning algorithm.
- Image Recognition Overview
- What is Machine Learning?
- Top 5 Uses of Image Recognition
- Are Machines becoming Smarter than Humans?
- Rising Popularity of Image Recognition
- Image Recognition Trends
- Prevent Crime and Improve Security with Facial Recognition
- Image Recognition in Medical Use
- Image Recognition Software on Cloud Platforms
- Image Recognition is Transforming Business
- Facial Recognition for Brand Awareness
- Image Recognition on Facebook
- Future of Image Recognition