Introduction
Image analysis is the process of identifying and classifying images to discover the intrinsic content of an image. You can use the output of the image analysis process for a variety of purposes, such as marketing, advertising, database mining, scientific research, and search engine optimization.
How does the analysis work?
Image analysis works by searching an image for key attributes of already known objects to provide a more accurate and reliable representation possible with low-level features such as corners or edges. An image is broken down into several regions or features, each examined for critical attributes, including shape and motion (both local and global). The results of this process are then interpreted as a classifier.
Features
(a) Image segmentation: the process of breaking an image into several regions where each region is divided into sub-regions; this occurs either manually or automatically to allow for more efficient detection and classification.
(b) Object recognition: the process of quantizing information of an image into a single object that the software can represent in terms of its shape.
Object recognition is similar to image segmentation but instead deals with the location and boundaries of objects rather than their area.
(c) Image character recognition: the process of quantizing an image into a sequence of characters, typically using features such as the shape descriptors; this is used in optical character recognition, for example.
(d) Image content analysis: the process of analyzing information contained within a single frame; this occurs by comparing an image to a set of benchmark images or a classifier.
Benefits
(1) You can use the output of the analysis to improve search engines as it would allow for better classification and retrieval of images.
(2) The output of the analysis can be used as a form of marketing research to provide hard data upon which to base future marketing campaigns.
(3) You can use the software in conjunction with image search engines, whose main benefit is the ability to refine search suggestions based upon human insight and experience.
(4) The software can be used to train other software, for example, to categorize images.
Cons
(1) The analysis is laborious and time-consuming. In order to analyze a large number of images, it may be necessary to employ more than one person.
(2) It is possible to train the software to be unreliable depending upon the initial training dataset. Unreliability can occur due to training on images that are not representative of the data space, such as extreme/unusual cases, or simply by including too few or too many examples for classification purposes.
(3) The output of the analysis can be used in many ways by different people.
Applications
(1) OCR/OCV: optical character recognition, used in banknotes and library catalogs to detect words and numbers; this is an early and widely used image analysis application.
(2) Object detection: the process of detecting objects within an image, for example, click detection in web images; this is widely used for advertising purposes where users are directed to a new page containing an advert.
(3) Image substitution: the process of replacing an object or a background in an image; this is used in advertising where an advertiser may want to change the product being advertised.
(4) Content-Based Image Retrieval: the process of retrieving images from a database depending upon their content, for example, color and segmentation;You could use this to retrieve images of cars from a motor manufacturer’s website.
Conclusion
Image analysis is a sophisticated process that can significantly benefit a company or brand by providing more accurate and reliable representations of images using computer software. You can use this software to improve search engines and measure specific attributes for marketing purposes, allowing for better planning of future campaigns.