Who this course is for: If any of these descriptions fit you, rest assured, PyImageSearch University is designed for you. You are a computer vision practitioner that utilizes deep learning and OpenCV at your day job, and you’re eager to level-up your skills.; You’re a developer who wants to learn computer vision/deep learning, complete your challenging project at work, and stand out from Who this course is for: If any of these descriptions fit you, rest assured, PyImageSearch University is designed for you. You are a computer vision practitioner that utilizes deep learning and OpenCV at your day job, and you’re eager to level-up your skills.; You’re a developer who wants to learn computer vision/deep learning, complete your challenging project at work, and stand out from Dec 01, · The term CBIR is commonly used in the academic literature, but in reality, it’s simply a fancier way of saying “image search engine”, with the added poignancy that the search engine is relying strictly on the contents of the image and not any textual annotations associated with the image
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Examples of Image Search Engines Image Search Engine Basics Tutorials. by Adrian Rosebrock on December 1, cbir phd thesis, Whether you are tagging and categorizing your personal images, searching for stock photos for your company website, or simply trying to find the right image for your next epic blog post, trying to use text and keywords to describe something that is inherently visual is a real pain.
I faced this pain myself last Tuesday as I was going through some old family photo albums there were scanned and digitized nine cbir phd thesis ago. You see, I was looking for a bunch of photos that were taken along the beaches of Hawaii with my family. I opened up iPhoto, and slowly made my way through the photographs. It was a painstaking process.
The meta-information for each JPEG contained incorrect dates. Perhaps by luck, I stumbled across one of the beach photographs, cbir phd thesis.
It was a beautiful, almost surreal beach shot. Puffy white clouds in the sky. Crystal clear ocean water, lapping at the golden sands. You could literally feel the breeze on your skin and smell the ocean air. After seeing this photo, I stopped my manual search and opened up a code editor. While applications such as iPhoto let you organize your photos into collections and even detect and recognize faces, we can certainly do more.
What if you could actually search your collection of images using an another image? Cbir phd thesis would allow you to apply visual search to your own images, in just a single click. I spent the next half-hour coding and when I was done I had created a visual search engine for my family vacation photos. I then took the sole beach image that I found and then submitted it to my image search engine. Within seconds I had found cbir phd thesis of the other beach photos, all without labeling or tagging a single image.
Sounds pretty hard to do, right? I mean, how do you quantify the contents of an image to make it search-able? In general, there tend to be three types of image search engines: search by meta-datasearch by exampleand a hybrid approach of the two. Searching by meta-data is only marginally different than your standard keyword-based search engines mentioned above. Search by meta-data systems rarely examine the contents of the image itself. Instead, they rely on textual clues such as 1 manual annotations and tagging performed by humans along with 2 automated contextual hints, such as the text that appears near the image on a webpage.
When a user performs a search on a search by meta-data system they provide a query, just like in a traditional text search engine, and then images that have similar tags or annotations are returned. A great example of a Search by Meta-Data image search engine is Flickr. After uploading an image to Flickr you are presented with a text field to enter tags describing the contents of images you have uploaded.
Flickr then takes these keywords, indexes them, and utilizes them to find and recommend other relevant images. Search by example systems, on the other hand, rely solely on the contents of the image — no keywords cbir phd thesis assumed to be provided. The image is analyzed, quantified, and stored so that similar images are returned by the system during a search. Image search engines that quantify the contents of an image are called Content-Based Image Retrieval CBIR systems, cbir phd thesis.
A great example of a Search by Example system is TinEye. TinEye is actually a reverse image search engine where you provide a query image, and then TinEye returns near-identical matches of the same image, along with the webpage that the original image appeared on.
Take a look at the example image at the top of this section. Here I have uploaded an image of the Google logo. So consider this: Are you going to manually label each of these 6 billion images in TinEye? Of course not. That would take an army of employees and would be extremely costly, cbir phd thesis. Then, when a user submits a query image, you extract features from the query image and compare them to your database of features and try to find similar images.
These types of systems tend to be extremely hard to build and scale, but allow for a fully automated algorithm to govern the search — no human intervention is required. On Twitter you can upload photos to accompany your tweets.
A hybrid approach would be to correlate the features extracted from the image with the text of the tweet. Using this approach you could build an image search engine that could take both contextual hints along with a Search by Example strategy. Cbir phd thesis Interested in reading more about the different types of image search engines?
I have an entire blog post dedicated to comparing and contrasting them, cbir phd thesis, available here. When building an image search engine we will first have to index our dataset. Indexing a dataset cbir phd thesis the process of quantifying our dataset by utilizing an image descriptor to extract features from each image.
An cbir phd thesis descriptor defines the algorithm that we are utilizing to describe our image. The important takeaway here is that the image descriptor governs how the image is quantified.
Featureson the other hand, are the output of an image descriptor. When you put an image into an image descriptor, you will get features out the other end, cbir phd thesis. In the most basic terms, features or feature vectors are just a list of numbers used to abstractly represent and quantify images.
Here we are cbir phd thesis with an input image, we apply our image descriptor, and then our output is a list of features used to quantify the image. Feature vectors can then be compared for similarity by using a distance metric or similarity function. Given two feature vectors, cbir phd thesis distance function is used to determine how similar the two feature vectors are.
The output of the distance function is a single floating point value used to represent the similarity between the two images. No matter what Content-Based Image Retrieval System you are building, they all can be boiled down into 4 distinct steps:. Again, these are the most basic 4 steps of any CBIR system. The figure below details Steps 1 and We start by taking our dataset of images, cbir phd thesis, extracting features from each image, and then storing these features in a database.
First, a user must submit a query image to our image search engine. We then take the query image and extract features from it. Finally, the results are then sorted by relevancy and presented to the user. This dataset consists of various vacation trips from all over the world, including photos of the Egyptian pyramids, underwater diving with sea-life, forests in the mountains, wine bottles and plates of food at dinner, boating excursions, and sunsets across the ocean.
In general, this dataset does an extremely good job at modeling what we would expect a tourist to photograph on a scenic trip. Our goal here is to build a personal image search engine. For instance, if I submit a photo of sail boats gliding across a river, our image search engine should be able to find and retrieve our vacation photos of when we toured the marina and docks. Take a look at the example below where I have submitted an photo of the boats on the water and have found relevant images in our vacation photo collection:, cbir phd thesis.
Because of this, we have to make an important assumption regarding our image cbir phd thesis engine:. Assumption: Images that have similar color distributions will be considered relevant to each other. This is a really important assumptionbut is normally a fair and reasonable assumption to make when using color histograms as image descriptors. Instead of using a standard color histogram, we are going to apply a few tricks and make it a little more robust and powerful. Our image descriptor will be a 3D color histogram in the HSV color space Hue, cbir phd thesis, Saturation, Value.
Typically, images are represented as a 3-tuple of Red, Green, and Blue RGB. However, while RGB values cbir phd thesis simple to understand, the RGB color space fails to mimic how humans perceive color. Instead, we are going to use the HSV color space which maps pixel intensities into a cylinder:. So now that we have selected a color space, we now need to define the number of bins for our histogram. Histograms are used to give a rough sense of the density of pixel intensities in an image.
Essentially, our histogram will estimate the probability density of the underlying function, or in this case, the probability P of a pixel color C occurring in our image I, cbir phd thesis. If you select too few binsthen your histogram will have less components and unable to disambiguate between images with substantially different color distributions.
Personally, I like an iterative, experimental approach to tuning the number of bins. This iterative approach is normally based on the size of my dataset. The more smaller that my dataset is, the less bins I use. Cbir phd thesis if my dataset is large, I use more bins, making my histograms larger and more discriminative. This means that for every image in our dataset, no matter if the image is 36 x 36 pixels or x pixels, all images cbir phd thesis be abstractly represented and quantified using only a list of floating point numbers.
I think the best way to explain a 3D histogram is to use the conjunctive AND. A 3D HSV color descriptor will ask a given image how many pixels have a Hue value that fall into bin 1 AND how many pixels have a Saturation value that fall into bin 1 AND how many pixels have a Value intensity that fall into bin 1. The number of pixels that meet these requirements are then tabulated.
This process is repeated for each combination of bins; however, we are able to do it in an extremely computationally efficient manner.
Open up a new file in your favorite code editor, name it colordescriptor. We then define our ColorDescriptor class on Line 6. This class will encapsulate all the necessary logic to extract our 3D HSV color histogram from our images. We can cbir phd thesis define our describe method on Line This method requires an imagewhich is the image we want to describe. Lines 18 and 19 simply grab the dimensions of the image and compute the center x, y coordinates. Using regions -based histograms rather than global -histograms allows us to simulate locality in a color distribution.
For example, take a look at this image below:.
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, time: 10:12Building image search an engine using Python and OpenCV - PyImageSearch
Who this course is for: If any of these descriptions fit you, rest assured, PyImageSearch University is designed for you. You are a computer vision practitioner that utilizes deep learning and OpenCV at your day job, and you’re eager to level-up your skills.; You’re a developer who wants to learn computer vision/deep learning, complete your challenging project at work, and stand out from Dec 01, · The term CBIR is commonly used in the academic literature, but in reality, it’s simply a fancier way of saying “image search engine”, with the added poignancy that the search engine is relying strictly on the contents of the image and not any textual annotations associated with the image Who this course is for: If any of these descriptions fit you, rest assured, PyImageSearch University is designed for you. You are a computer vision practitioner that utilizes deep learning and OpenCV at your day job, and you’re eager to level-up your skills.; You’re a developer who wants to learn computer vision/deep learning, complete your challenging project at work, and stand out from
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