In the past decades of the digital era, the amount of electronic data such as text, audio, images, and many more have increased to tremendous amounts. A study reveals that the number of cameras in the world exceeds the number of eyes. Reports suggest there will be approximately 7.4 trillion images generated by the end of 2020. An image retrieval system is used to search for images similar to the query image in a large image database. An image retrieval system will assist in the processing, organizing, and handling of image data efficiently. Companies such as Google and Pinterest use image retrieval systems to provide users with related images. In the year 1970, a text-based image retrieval technique was implemented where images were first annotated manually and then searched using a text query. This method was extremely time-consuming, labor-intensive, and prone to errors in annotation due to human perception's subjectivity. In the early 1980s, a content-based image retrieval (CBIR) system was introduced which used visual features extracted from an image. This method used traditional image processing tools for understanding the color, texture, and shape-based features of the image. This approach had problems such as inappropriate and limited feature representations which resulted in less efficient and non-generalized algorithms. Advancement in deep learning (DL) related to image processing has given rise to better processing of image data. Deep learning was inspired by the working of the biological neural network. It is widely used for classification tasks and achieves state-of-the-art performance. Today, we use convolutional neural networks (CNN) to perform image retrieval tasks. CNN is a variant of DL and is widely used for computer vision applications because of its inherent ability to extract features from an image. CNN has also shown prominent results as compared to traditional image processing techniques. In a CNN, convolutional layers are used for feature representation and extraction. After the features are extracted, distance metrics are used for measuring the similarity between the query image and the images from the database. Generally, similarity metrics such as Euclid distance, Cosine distance, and Manhattan distance facilitate finding similar images from the database. Further, the authors will discuss the image retrieval system using convolutional auto-encoders and improving an image retrieval system's usability using generative adversarial networks (GANs). In convolutional autoencoders, the autoencoder's encoder portion is used to compute the latent space representation, which quantifies the content of the image in a feature vector. The query image's feature vector is compared with the feature vectors of all the images in the database to find similar images. GANs can produce an image from text or a simple sketch. Alternatively, GANs can be used to create an image with new features. The image generated by a GAN can be used for image retrieval thereby adding a method to query an image retrieval system. Convolutional neural networks, convolutional auto-encoders, and GANs are the three prominent methodologies for image retrieval that are to be discussed by the authors.

Chapter Contents:

• Abstract
• 3.1 Introduction
• 3.2 Conventional content-based image retrieval (CBIR) system
• 3.2.1 Semantic-based image retrieval (SBIR) system
• 3.3 Deep learning
• 3.4 Image retrieval using convolutional neural networks (CNN)
• 3.5 Image retrieval using autoencoders
• 3.6 Image retrieval using generative adversarial networks (GAN)
• References

Inspec keywords: image retrieval; deep learning (artificial intelligence); feature extraction; visual databases; vectors; content-based retrieval; convolutional neural nets

Other keywords: deep neural networks; deep learning; image processing tools; text-based image retrieval; convolutional neural network; intelligent image retrieval system; generative adversarial network; image database; query image; CBIR system; content-based image retrieval system

Subjects: Image recognition; Computer vision and image processing techniques; Spatial and pictorial databases; Algebra; Information retrieval techniques; Algebra; Neural nets