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Examples of Image Search Engines Image Search Engine Basics Tutorials. by Adrian Rosebrock on December 1, Whether you are tagging and categorizing your personal images, searching for stock photos for your company website, cbir phd thesis, 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 cbir phd thesis digitized nine years 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, cbir phd thesis. The meta-information for each JPEG contained incorrect dates. Perhaps by luck, I stumbled across one of the beach photographs.
It was a beautiful, almost surreal beach shot. Puffy white clouds in the sky, cbir phd thesis. Crystal clear ocean water, cbir phd thesis, 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? It 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. Cbir phd thesis then took the sole cbir phd thesis image that I found and then submitted it to my image search engine.
Within seconds I had found all of the other beach photos, all without labeling or tagging a single image, cbir phd thesis. 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 cbir phd thesis systems rarely examine the contents of the image itself, cbir phd thesis. 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, cbir phd thesis, and utilizes them to find and recommend other relevant images. Search cbir phd thesis example systems, on the other hand, rely solely on the contents of the image — no keywords are 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. 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 cbir phd thesis matches cbir phd thesis 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, cbir phd thesis. That would take an army of employees and would be extremely costly. Then, when a user submits a query image, cbir phd thesis, 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. Note: Interested in reading more about the different types of image cbir phd thesis engines? I have an entire blog post dedicated to comparing and contrasting them, available here. When building an image search engine we will first have to index our dataset.
Indexing a dataset is the process of quantifying our dataset by utilizing an image descriptor to extract features from each image. An image 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.
In the most basic terms, features or feature vectors are just a list of numbers used to cbir phd thesis represent and quantify images. Here we are presented 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 cbir phd thesis for similarity by using a distance metric or similarity function.
Given two feature vectors, a 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, cbir phd thesis, 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, 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, cbir phd thesis. We then take the query image and extract features from it. Finally, the results are then sorted cbir phd thesis 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, cbir phd thesis, 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 search 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 cbir phd thesis little more robust and powerful.
Our image descriptor will be a 3D color histogram in the HSV color space Hue, Saturation, Value. Typically, images are represented as a 3-tuple of Red, Green, and Cbir phd thesis RGB. However, while RGB values are simple to understand, the RGB color space fails to mimic how humans perceive color. Instead, cbir phd thesis, 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, cbir phd thesis, our histogram will estimate the probability density of the underlying function, or in this case, the probability P cbir phd thesis a pixel color C occurring in our image I.
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, cbir phd thesis, 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.
And 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, cbir phd thesis, no matter if the image is 36 x 36 pixels or x pixels, all images will 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 cbir phd thesis 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 cbir phd thesis 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, cbir phd thesis. We can then 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, cbir phd thesis coordinates.
Using regions -based histograms rather than global -histograms allows us to simulate locality in a color distribution. For example, cbir phd thesis, take a look at this image below:.
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