"Image understanding (IU) is the research area concerned with the design and experimentation of computer systems that integrate explicit models of a visual problem domain with one or more methods for extracting features from images and one or more methods for matching features with models using a control structure. Given a goal, or a reason for looking at a particular scene, these systems produce descriptions of both the images and the world scenes that the images represent."
– Image Understanding, by J.K. Tsotos. In Encyclopedia of Artificial Intelligence. Stuart C. Shapiro, editor. 1987. New York: John Wiley & Sons.
This article was published as a part of the Data Science Blogathon. In this blog, we will be discussing how to perform image classification using four popular machine learning algorithms namely, Random Forest Classifier, KNN, Decision Tree Classifier, and Naive Bayes classifier. We will directly jump into implementation step-by-step. At the end of the article, you will understand why Deep Learning is preferred for image classification. However, the work demonstrated here will help serve research purposes if one desires to compare their CNN image classifier model with some machine learning algorithms.
For creators, brand marketers, and business owners, producing content is only half the journey. To ensure that the videos serve their intended purpose, video-makers must also consider several other factors--testing, targeting, scheduling, and distribution, among other things. One particularly important step that's usually foregone by smaller creators and businesses is testing. This is usually due to the limited, expensive, and time-consuming nature of traditional market research. In this article, we'll demonstrate how Aifilia's technology can provide anyone working with video content an efficient and proven means to test their creations.
In this post we discuss SwAV (Swapping Assignments between multiple Views of the same image) method from the paper "Unsupervised Learning of Visual Features by Contrasting Cluster Assignments" by M. Caron et al. For those interested in coding, several code repositories about SwAV algorithm are on GitHub; if in doubt, take a look at the repo mentioned in the paper. Supervised learning works with labeled training data; for example, in supervised image classification algorithms a cat photo needs to be annotated and labeled as "cat". Self-supervised learning aims at obtaining features without using manual annotations. We will consider, in paticular, visual features.
Hi, What will I provide you? I will provide object detection projects with modern algorithms, including (Yolov4 darknet, Yolov5 PyTorch, SSD, etc.). The delivery code implementation will be in TensorFlow (for yolov4) or PyTorch (for yolov5). What projects have I worked on? I have done many computer vision projects, but significant mentioned below, 🌟 People detection, counting with custom training of yolov4, yolov5 🌟 Multi-classes detection including (Head, body, person). 🌟 Multiple objects detection with tracking using yolov5 and Kalman. 🌟 Deployed Yolov5, Yolov4, SSD on edge devices, including cameras, jetson devices. The project output will include the python code of the developed model, the dataset for your testing purpose, model files, and a brief report regarding steps in code for better understanding. Model files will also be in different formats according to buyer requirements like (.h5, .pt, .onnx, .torchscript etc). Thanks, Regards.
We are going to train custom image classification models using transfer learning technique and ConvNets (Convolutional Neural Nets). Note that there is a parameter for undersampling. So you don't need to under sample manually before running the script. It is passed to the RandomUnderSampler class within imblearn library. You'll probably need to change some parameters here, depending on the complexity of your dataset and its labels.
We generally see artificial intelligence classifying and predicting, but creating? This is called neural style transfer. NST (Neural Style Transfer) transfers a style from a picture to another- using neural networks to transfer the style. The content image is the image that receives the style, and the image whose style is transferred is called the style image. No matter what style image you pair up with the content image, the content of the content image will still be apparent in the newly generated image.
Multi-object tracking (MOT) in video analysis is increasingly in demand in many industries, such as live sports, manufacturing, surveillance, and traffic monitoring. For example, in live sports, MOT can track soccer players in real time to analyze physical performance such as real-time speed and moving distance. Previously, most methods were designed to separate MOT into two tasks: object detection and association. The object detection task detects objects first. The association task extracts re-identification (re-ID) features from image regions for each detected object, and links each detected object through re-ID features to existing tracks or creates a new track.
Image classification or computer vision is a branch of artificial intelligence where the task is to design systems that can recognise or classify objects based on digital images. It is a popular field due to the sheer breadth of applications -- image classification can be used for applications as diverse as recognising a particular flower from a photograph or to classifying medical images as normal or disease.