Python Advanced Image Processing

Building an image search engine is not an easy task. Here are a few concepts, tools, ideas, and techniques to implement. One of the main image processing concepts is inverse image query (Riq). Companies such as Google, Cloudera, Sumo Logic and Birst are among the top in using inverse image search. The analysis of images and the use of data mining Riq provide excellent insight and analysis capabilities.

There are a lot of top technology companies that use Riq to make good profits. For example: In 2014 Pinterest first brought a visual search. A white paper was subsequently published in 2015, revealing its structure. Inverse image Search allows Pinterest to obtain the visual characteristics of fashion products, and can display similar product recommendations.

As we all know, Google images using inverse image search allows users to upload a picture and then search for associated images. By using advanced algorithms to analyze and mathematically model submitted images, then compare them with countless other images in Google's database to get similar results.

Before we use it to work, let's go through the main elements of the Python library that builds the image search engine:

algorithm Scaling invariant feature transformation algorithm

1. A patented technology with non-free functionality, using image identifiers to identify similar images, even those from different angles, sizes, depths and scales, will also be included in the search results. Click here to view SIFT detailed video.
2. SIFT can match search criteria correctly with large databases that extract features from many images.
3. It is another feature of SIFT that the same images and matching invariant features can be matched to different viewing angles to obtain search results. Learn more about scale invariant key points.

accelerated robust feature (SURF) algorithm

1. SURF is also a patented technology with a non-free function, and a SIFT version of "acceleration". Unlike Sift,surf, which is close to the Laplace operator with a box-type filter.
2. The SURF relies on the position and scale of the black plug matrix.
3. In many applications, rotational invariance is not a necessary condition, so not finding in this direction accelerates processing.
4. SURF includes several features that increase the speed of each step. SIFT is doing well in terms of rotation and blurring, three times times faster than the SIFT. However, it is not good at dealing with lighting and changing perspectives.
5. The OpenCV Program feature library provides SURF functionality, Surf.compute () and SURF. Detect () can be used to find descriptors and points. Read more about Surf click here

Kaze algorithm

1. Kaze is an open-source nonlinear scale space with two-dimensional multiscale and new feature detection and description algorithms. Effective techniques in additive operator splitting (AOS) and variable conductance diffusion are used to establish a nonlinear scale space.
2. The basic principle of Multiscale image processing is simple: Create a scale space for an image and filter the original image with the correct function to improve time or scale.

accelerated (AKAZE) algorithm

1. As the name implies, this is a faster way to search the image, it will find a match between the two images of the key points. AKAZE uses binary descriptors and nonlinear scale spaces to balance precision and speed.

binary robust Invariant scale variable critical point (brisk) algorithm

1. The brisk is ideal for the description, detection, and matching of key points.
2. is a highly adaptive algorithm, fast detector based on scale space and a bit string descriptor, which helps to significantly speed up the search.
3. Scale space key point detection and key point descriptions help optimize the performance of current related tasks.

Rapid Retinal key point (FREAK)

1. This new key point describes the inspiration from the human eye. A binary cascade cascade can be calculated efficiently by the image strength ratio. The FREAK algorithm can be compared to the brisk, SURF, and SIFT algorithms for faster computation and lower memory load.

directional FAST and rotary BRIEF (ORB)

1. Fast binary descriptor, ORB has anti-noise and rotational invariance. The ORB is built on FAST key detectors and BRIEF descriptors, with low-cost, well-performing element attributes.
2. In addition to fast and accurate positioning elements, the efficient calculation of directional BRIEF, analysis of changes and orientation to the BRIEF feature is associated with another ORB feature.

Python library OpenCV

1. OpenCV supports academic and commercial purposes and is an open-source machine learning and computer Vision Library that OpenCV facilitates the organization of the use and modification of code.
2. More than 2,500 optimized algorithms, including the most advanced machine learning and computer vision algorithm services and a variety of image search-face detection, target recognition, camera target tracking, from the image database to find similar images, eye movement following, landscape recognition and so on.
3. Big companies such as Google, IBM, Yahoo, Sony, Honda, Microsoft and Intel are using OpenCV extensively.
4. OpenCV has python,java,c,c++ and MATLAB interfaces, while supporting Windows,linux,mac OS and Android.

Python Image Library (PIL)

1. The Python Image Library (PIL) supports a variety of file formats while providing image processing and graphics solutions. The open source PIL adds image processing power to your Python interpreter.
2. Standard image processing capabilities include image enhancement, transparency and masking processing, image filtering, pixel manipulation, and more.

For detailed data and graphs, see the OpenCV 2.4.9 feature comparison report here.

Build an image search engine

Image search engines can select similar images from a pre-built image library. One of the most popular is Google's famous image search engine. For starters, there are different ways to build such a system. Mention a few of the following:

1. The image search engine is established by image extraction, image description extraction, metadata extraction and search result extraction.
2. Define your image descriptor, DataSet index, define your similarity metrics, and then search and rank.
3. Select the image you want to search, select the directory to search for, search the catalog of all the pictures, create a picture feature index, evaluate the same characteristics of the search image, match the search image and get a matching picture.

Our approach has evolved from comparing grayscale versions of images to complex feature matching algorithms such as SIFT and SURF, and finally using open source solutions brisk. All of these algorithms provide effective results, but slight changes in performance and latency. The engines built on these algorithms have many applications, such as analyzing the graphical data of popular statistics, identifying objects in graphical content, and so on.

Example : An IT company built an image search engine for its customers. Therefore, if you search for a brand logo image, all relevant branding images should also be displayed in the search results. The resulting results can also be used by customers for analysis, allowing them to estimate brand awareness based on geographic location. But it is also relatively young, and the potential of Riq (reverse image search) has not yet been fully exploited.

This concludes our article, using Python to build an image search engine. Browse our blog section to see the latest programming techniques.

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Python Advanced Image Processing