You can use the Computer Vision Toolbox™ functions to search by image, also known as a content-based image retrieval (CBIR) system. CBIR systems are used to retrieve images from a collection of images that are similar to a query image. The application of these types of systems can be found in many areas such as a web-based product search, surveillance, and visual place identification. First the system searches a collection of images to find the ones that are visually similar to a query image.
The retrieval system uses a bag of visual words, a collection of image descriptors, to represent your data set of images. Images are indexed to create a mapping of visual words. The index maps each visual word to their occurrences in the image set. A comparison between the query image and the index provides the images most similar to the query image. By using the CBIR system workflow, you can evaluate the accuracy for a known set of image search results.
Create image set that represents image features for
imageDatastore to store the
image data. Use a large number of images that represent various viewpoints
of the object. A large and diverse number of images helps train the bag of
visual words and increases the accuracy of the image search.
Type of feature.
indexImages function creates
the bag of visual words using the speeded up robust features (SURF).
For other types of features, you can use a custom extractor, and then
bagOfFeatures to create
the bag of visual words. See the Create Search Index Using Custom Bag of Features example.
You can use the original
imgSet or a different
collection of images for the training set. To use a different collection,
create the bag of visual words before creating the image index, using
The advantage of using the same set of images is that the visual vocabulary
is tailored to the search set. The disadvantage of this approach is
that the retrieval system must relearn the visual vocabulary to use
on a drastically different set of images. With an independent set,
the visual vocabulary is better able to handle the additions of new
images into the search index.
Index the images.
indexImages function creates
a search index that maps visual words to their occurrences in the
image collection. When you create the bag of visual words using an
independent or subset collection, include the
an input argument to
indexImages. If you do not
create an independent bag of visual words, then the function creates
the bag based on the entire
imgSet input collection.
You can add and remove images directly to and from the image index
Search data set for similar
images. Use the
to search the image set for images which are similar to the query
image. Use the
NumResults property to control
the number of results. For example, to return the top 10 similar images,
ROI property to use a smaller region
of a query image. A smaller region is useful for isolating a particular
object in an image that you want to search for.
to evaluate image retrieval by using a query image with a known set
of results. If the results are not what you expect, you can modify
or augment image features by the bag of visual words. Examine the
type of the features retrieved. The type of feature used for retrieval
depends on the type of images within the collection. For example,
if you are searching an image collection made up of scenes, such as
beaches, cities, or highways, use a global image feature. A global
image feature, such as a color histogram, captures the key elements
of the entire scene. To find specific objects within the image collections,
use local image features extracted around object keypoints instead.