Build a Similar Images Finder with Python, Keras, and Tensorflow

Build your own image similarity application using Python to search and find images of products that are similar to any given product. You will implement the K-Nearest Neighbor algorithm to find products with maximum similarity.

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Finding Similar Images Project Template Outcomes

  • KNN Overview
  • Higher Dimensional Database - Overview
  • ANN BenchMarks and libraries of HDDB
  • Downloading Imaterialist using Python Script
  • Understanding MobileNet Architecture
  • Understanding Feature Extraction
  • Setting up ElasticSearch with a plugin for KNN
  • How to connect to ElasticSearch using Python
  • Indexing Using ElasticSearch with Python
  • Querying ElasticDb over Knn with Python
  • ElasticSearch API in action and understanding ImageSearch Response

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Project Description

Detect Similar Images Python Project- Business Objective

The objective of the similar images python project is to develop a computer vision system that can effectively and precisely identify products based on their images at the individual stock-keeping-unit (SKU) level, in response to the growing trend of online shopping and e-commerce. By implementing this computer vision project, we aim to address the market demand for automated and accurate product recognition. The primary focus of this python find similar images project is to enable users to search and discover images of products that closely resemble a given input image.

For example, imagine a user has a photo of a pair of shoes they like and want to find similar products online. The project aims to create a solution that can analyze the image, understand its key features, and provide a list of other products with similar visual characteristics, such as design, color, and style. This way, users can effortlessly explore and find alternative options that match their preferences based on the visual similarity of products.

Goal of Similar Image Finder Project

To find images similar to any given image from the database.

Find Similar Image Python Project- Use Cases

Image Similarity Search Python project has several use cases across various domains:

  • E-commerce: Similar Image Finder applications can be used in e-commerce platforms to improve product search and recommendation systems.Identifying visually similar images helps users find alternative or visually similar products, enhancing their shopping experience.

  • Content Management: Similar Image Finder can be utilized in content management systems to detect duplicate or near-duplicate images. It is beneficial for identifying and managing redundant images, ensuring efficient storage, and eliminating duplicate content.

  • Intellectual Property Protection: Similar Image Finder can assist in detecting copyright infringement by comparing uploaded images with a database of copyrighted images. It helps identify instances of unauthorized image usage and protects intellectual property rights.

  • Visual Analytics: Similar Image Finder can aid in visual analytics tasks such as image clustering, grouping similar images together based on visual similarity. It enables the efficient organization and exploration of large image datasets.

  • Forensics and Security: Similar Image Finder can be employed in forensic investigations and security applications to identify visually similar images or individuals across different sources. It can help in identifying potential threats, monitoring suspicious activities, or linking related visual information.

  • Art and Design: Finding duplicates of images can benefit art and design domains, allowing artists, designers, or researchers to explore and find visually similar artwork, design elements, or inspiration from a vast collection of images.

  • Social Media Analysis: Similar Image Finder can be used in social media platforms to detect and manage similar or duplicate images shared by users. It aids in preventing spam, identifying popular trends, and improving content moderation.

Applications of Finding Similar Images using Deep Learning

Companies across different industries utilize Similar Image Finder for various purposes. Here are some examples:

  • Online marketplaces like Amazon and eBay use Similar Image Finder to enhance product recommendation systems. They can improve customer engagement and increase sales by suggesting visually similar products based on customer preferences and browsing history.

  • Platforms like Facebook and Instagram use Similar Image Finder to detect and manage duplicate or similar images users share. It helps prevent spam, maintain content quality, and enhance user experience.

  • Companies managing large image databases, such as stock photo agencies or media organizations, leverage Similar Image Finder to identify duplicate or near-duplicate images. This streamlines content organization, eliminates redundancy, and optimizes storage space.

  • Fashion brands like Zara or ASOS utilize Similar Image File Finder to provide personalized recommendations to customers. They can offer a seamless shopping experience and increase customer satisfaction by suggesting visually similar clothing items or accessories.

  • Security companies and law enforcement agencies employ Similar Image Finder for facial recognition and image matching purposes. It helps identify individuals of interest from surveillance footage or compare images (faces) from input image against watchlists to enhance security measures.

Tech Stack for Project on Finding Similar Images

  • Language : Python

  • Cloud support : AWS

  • Libraries : Elasticsearch, Tensorflow, Keras, Numpy, Pandas, Requests, Scikit-learn

Data Overview for Finding Most Similar Images Python Project

The dataset includes images from 2,019 product categories with one ground truth class label for each image. It includes a total of 1,011,532 images for training, 10,095 images for validation and 90,834 images for testing.

It is to be noted that only the URL is provided for each image. Users need to download the images by themselves. It is also to be noted that the image URLs may become unavailable over time. 

Data Source: https://www.kaggle.com/c/imaterialist-product-2019/overview

Learning Takeaways from Python Image Similarity Project

Let us discuss the key concepts this Keras Image Similarity Project will help you learn.

Introduction to Machine Learning

In this build a similar images finder ML project, the concept of machine learning is introduced, focusing on two main categories: supervised learning and unsupervised learning. Supervised learning involves training a model using labeled data, where the input and corresponding output are provided. The project will teach you the difference between the classification and regression algorithms in supervised learning. On the other hand, unsupervised learning deals with unlabeled data, where the model learns patterns, structures, or relationships in the data without explicit guidance. You will learn about various clustering algorithms used in unsupervised learning.

KNN Algorithm

The K-nearest neighbor search algorithm is a simple yet powerful machine learning technique used for solving supervised learning problems. It finds the K closest training examples in the feature space and predicts the label or value based on the majority or average of their neighbors. All the steps used to predict the output value will be explained in this project solution. You will learn about the significance of the model in building a similar image finder project solution.

Database

There are popularly two types of databases that are used in the industry- NoSQL and SQL. This Similar Image Search Python Project will cover the explanation of the two in detail. As you know, the images usually take up more space and thus lead to a database of higher dimensions. This project will introduce the higher dimensional database and embedding space. You will learn the significance of a lower distance between two visually similar images in the embedding space. You will also get to explore the list of popularly used HDDBs in the industry that support the implementation of the KNN algorithm.

Elasticsearch

Elasticsearch with KNN support is an extension of Elasticsearch that enables the use of K-nearest neighbors (KNN) algorithm for similarity search. It allows efficient indexing and querying of high-dimensional data. The direct installation of ElasticSearch does not support KNN. So, this Python Find Similar Images project will help you understand how to set up ElasticSearch with KNN support in a beginner-friendly manner. You will also be guided on installing the prerequisites for utilizing Elasticsearch with KNN support. There will also be a discussion of an overview of Elasticsearch from Amazon documentation. You will also get to learn how to connect to Elasticsearch, index it and query it.

Preparing the Dataset

As mentioned above, the data in this project contains URLs to various images, which must be downloaded individually. Instead of completing this task manually, the project will guide you through a Python script that will automate this task.

MobileNet Architecture

MobileNet is a popular deep-learning model specifically designed for mobile and embedded devices. It achieves a good balance between accuracy and computational efficiency by utilizing depthwise separable convolutions, making it suitable for real-time applications with limited computational resources. The first two versions of the model will be explained in depth as the different layers that form the architecture will be discussed. You will learn how to preprocess the images for feature extraction before serving them as an input to the MobileNet model.

DJango

Django is a high-level Python web framework that simplifies and speeds up the development of web applications. It follows the model-view-controller (MVC) architectural pattern, providing features like an ORM, URL routing, templating, and built-in security measures, making it a popular choice for building robust and scalable web applications. In this project, you will learn how to implement the full solution over the web using Django. All the necessary APIs will be explained in detail. Besides that, you will also learn how to implement this project solution on any dataset.

Solution Approach to Similar Image Finder Project

  1. Download images from label_id - Downloading all the images using the given URLs of images.

  2. Indexing using ElasticSearch - Feature extraction is done using the weights of imagenets from MobileNetV

  3. Image2Image Query - Use K Nearest Neighbour in Elastic search to find K nearest vectors which are having maximum similarity for the queried image.

FAQs on Find Similar Images Project in Python

1. How to do image similarity in Python?

To perform image similarity in Python, you can use libraries such as OpenCV and scikit-image. Common approaches include calculating structural similarity index (SSIM) using functions like compare_ssim() or pixel-wise mean squared error (MSE) using functions like mean_squared_error(). Deep learning-based approaches involve using pre-trained CNN models and extracting feature embeddings for image comparison.

2. How do you measure image similarities?

Image similarities can be measured using various techniques in Python. Common methods include calculating the structural similarity index (SSIM), pixel-wise mean squared error (MSE), normalized cross-correlation (NCC), histogram-based approaches (such as histogram intersection or chi-square distance), and deep learning-based approaches using pre-trained convolutional neural networks (CNNs) like VGG or ResNet.

3. What is the similarity index in Python?

In Python, the similarity index is a measure that quantifies the likeness or resemblance between two objects, such as images. It helps determine the level of similarity between the objects based on certain features or characteristics. Various methods and algorithms can be employed to calculate the similarity index.

 

Finding Similar Images

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