Imagine yourself walking around in safari with a bunch of different animals all over the park. Suddenly a lion approaches you and you might be trembling with fear. This is a response from your brain in milliseconds in such a terrifying situation. Typically, if someone expects a similar kind of response from machines where it identifies certain objects or classifies them into different entities the answer would be simply ‘NO’ if it was anticipated decades ago.
In this 21st century with lots of advancements in Artificial Intelligence and Computer Vision has turned around the nature of machines and its results are revolutionary. We are able to develop models in computer vision where object identification is not enough, it is important to analyze the object and draw its granular pattern. Image segmentation is actually partitioning the specific segments in an image that translate into more meaningful patterns and is easier to explore.
For instance; there is a single object in image i.e. Chair we can simply construct a model that depicts a chair in the figure.
But what if there is a chair and table as well in the picture, here we need to instruct our multi-label classifier to identify each object in the image.
Additionally, if we add a location to the same image here is another field subject to identification by a classifier. Here image localization comes in;
It’s easier to identify a single image with location as compared to multiple images. For multiple images in a location, we can identify it with the help of object identification (automated system of positioning an image with respect to its location). So location is predicted with reference to its class with Object Identification.
Image similarity refers to the comparison between how identical the two images are. Image similarity measures the level of intensity in between those images. As two images are compared it gives value and indicates how similar these images are. If the values fall close to zero, they are more parallel and if the scores are ‘0’ images are identical.
Image similarity can be done in the following three steps;
Compare two images; Initially two images will be compared and outputs that we will receive will be distance in between these two images that reflects the similarity of two objects.
Run user data; Image datasets will be processed and the numbers will highlight how similar the images are. Exploring duplicate datasets are at times is very stricken activity, here similarity algorithm does its due part and analyzes results.
Image Search; After extracting results, you need to evaluate how similar the images are by comparing its resultant value. You can have a better comparison after looking at before and after results.
Major setback of image similarity the comparison between similar category images here raw pixels play a part and it performed poorly in applications. Here, feature engineering is most important. Feature engineering with image data requires domain expertise and software domain specific knowledge.