For instance, if we were to use VGG-16in place of ResNet-50, the feature vector would have reduced to 512-dimensions. We only need to keep in mind aspects such as the domain of the pre-trained networks and our dataset for reverse image search along with the dimensionality of the output vector.
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Note: we leveraged ResNet-50 to obtain image vectors but we are free to use any other pre-trained model. The 2048-dimensional vector is obtained from the average pooling layer of the ResNet model which was instantiated without the top classification layers. As shown, the function transforms the image input into a 2048-dimensional dense vector output.
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If we look closely at the code listing 1, the high-level tensorflow.keras API makes it extremely easy to prepare an instance of the pre-trained model and use it for our intended use of feature extraction. Code listing 1 presents the implementation for the same.Ĭode Listing 1: Transform Image into a feature vector using pre-trained ResNet-50 Next we need a utility function to pre-process the input image and get the vector output. We first need to prepare an instance of the pre-trained ResNet-50 model. This process of transforming inputs to vectors is also called vectorisation. Such features are usually dense 1-dimensional vectors. Let us leverage the pre-trained ResNet-50 to transform images into features. The reverse-image search use-case is a clever utilisation of this feature extraction property itself. The reverse-image search use-case is a clever utilisation of feature extraction property of pre-trained models (Transfer Learning). With the help of transfer learning, we can train highly performant classifiers composed of a pre-trained network as feature extractor followed by a few shallow layers. Revisiting feature extraction in case of CNNs, we know how the initial set of layers from a pre-trained model become adept at understanding images. Transfer learning can be leveraged in feature extraction, fine-tuning and pre-trained settings. Let us now explore image similarity from the deep learning lens. For instance, two different human models in different poses might be showcasing the same shirt/dress which may turn out to be difficult for traditional techniques to handle.Īs you probably know, one can try to identify similar images using metadata-based methods or even using traditional computer-vision based techniques. On the other hand, objects which are not so rigid are hard to match. For instance, identifying images with rectangular boxes such as packaging cartons or circular disks such as watch faces, is very effective with such methods. These are fast and powerful methods which are extremely useful in scenarios where we have rigid objects. The top 50 matching features between two images has been shown using colored lines. The example shows two photographs of Statue of Liberty taken from different perspectives. It works extremely well for rigid or unchanging objects. SIFT identifies key features highlighted as coloured circles on each of the candidates and then compares them to find similar ones.įigure 1: SIFT based features for image similarity. Figure 1 depicts how SIFT based features can be leveraged for finding similar images. SIFT, SURF and a number of other such traditional methods are adept at extracting key features from an input image. Traditional computer vision algorithms such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) are quite robust at extracting features which can be compared to identify similar images.
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The most obvious one being the huge manual effort required to tag each image.Ī more sophisticated, computer-vision based technique is to extract different features from the image itself. This approach is simple with a number of pitfalls.
Such applications typically store such metadata in optimised text search platforms such as ElasticSearch¹ or Solr². This is still being leveraged as a way of reverse image search by a number of web applications. In other words, we could associate metadata information such as image size, RGB values, category tags, and so on with every image. How do we do so programmatically?Ī very naïve approach would be based on metadata associated with the images. But how does one define image similarity? Given two sample images, visually we can easily determine if they are similar or not. Image Credits : Becca Tapert on Unsplash Image SimilarityĪs you might be aware, the problem of reverse image search is about finding similar images.
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