"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.
Therefore, in this article, we focus on how to use a couple of utility methods from the Keras (TensorFlow) API to streamline the training of such models (specifically for a classification task) with a proper data pre-processing. In the end, we aim to write a single utility function, which can take just the name of your folder where training images are stored, and give you back a fully trained CNN model. We use a dataset consisting of 4000 images of flowers for this demo. The dataset can be downloaded from the Kaggle website here. The data collection is based on the data Flickr, Google images, Yandex images.
Neural style transfer involves transferring the style of one image onto the content of the other. Content is literally what the image is made up of. It might be a scenery, a beach environment, a cat in a garden, a giraffe in a zoo…..etc. In your typical image classification network, this is basically the label of your image. Content is a high level representation of what an image is made up of.
As we scale AI and machine learning to work on a broader set of tasks for enterprise and industry applications, it is imperative to learn more from less. Data augmentation is one important tool, especially in situations where there isn't enough training data, that improves learning by synthesizing new training samples automatically. Such is the case for few-shot learning, where only one or very few samples are available per category. Most prior work on few-shot classification for images investigates the'single label' scenario, where every training image contains only a single object and hence has a single category label. However, a more challenging and realistic scenario is multi-label, few-shot image classification where training data has a small number of samples, and images have more than one label, which has not been explored extensively in prior work.
Likewise, we admire the story of musicians, artists, writers and every creative human because of their personal struggles, how they overcome life's challenges and find inspiration from everything they've been through. That's something that can't be automated, even if we achieve the always-elusive general artificial intelligence. This article will be a tutorial on using neural style transfer (NST) learning to generate professional-looking artwork like the one above. NST has been around for a while and there are websites that perform all of the functions before you, however, it is very fun to play around and create your own images. NST is quite computationally intensive, so in this case, you are limited not by your imagination, but primarily by your computational resources.
With the recent advances in complex networks theory, graph-based techniques for image segmentation has attracted great attention recently. In order to segment the image into meaningful connected components, this paper proposes an image segmentation general framework using complex networks based community detection algorithms. If we consider regions as communities, using community detection algorithms directly can lead to an over-segmented image. To address this problem, we start by splitting the image into small regions using an initial segmentation. The obtained regions are used for building the complex network. To produce meaningful connected components and detect homogeneous communities, some combinations of color and texture based features are employed in order to quantify the regions similarities. To sum up, the network of regions is constructed adaptively to avoid many small regions in the image, and then, community detection algorithms are applied on the resulting adaptive similarity matrix to obtain the final segmented image. Experiments are conducted on Berkeley Segmentation Dataset and four of the most influential community detection algorithms are tested. Experimental results have shown that the proposed general framework increases the segmentation performances compared to some existing methods.
Since their introduction more than a decade ago, smartphones have been equipped with cameras, allowing users to capture images and video without carrying a separate device. Thanks to the use of computational photographic technologies, which utilize algorithms to adjust photographic parameters in order to optimize them for specific situations, users with little or no photographic training can often achieve excellent results. The boundaries of what constitutes computational photography are not clearly defined, though there is some agreement that the term refers to the use of hardware such as lenses and image sensors to capture image data, and then applying software algorithms to automatically adjust the image parameters to yield an image. Examples of computational photography technology can be found in most recent smartphones and some standalone cameras, including high dynamic range imaging (HDR), auto-focus (AF), image stabilization, shot bracketing, and the ability to deploy various filters, among many other features. These features allow amateur photographers to produce pictures that can, at times, rival photographs taken by professionals using significantly more expensive equipment.
Reducing the latency variance in machine learning inference is a key requirement in many applications. Variance is harder to control in a cloud deployment in the presence of stragglers. In spite of this challenge, inference is increasingly being done in the cloud, due to the advent of affordable machine learning as a service (MLaaS) platforms. Existing approaches to reduce variance rely on replication which is expensive and partially negates the affordability of MLaaS. In this work, we argue that MLaaS platforms also provide unique opportunities to cut the cost of redundancy. In MLaaS platforms, multiple inference requests are concurrently received by a load balancer which can then create a more cost-efficient redundancy coding across a larger collection of images. We propose a novel convolutional neural network model, Collage-CNN, to provide a low-cost redundancy framework. A Collage-CNN model takes a collage formed by combining multiple images and performs multi-image classification in one shot, albeit at slightly lower accuracy. We then augment a collection of traditional single image classifiers with a single Collage-CNN classifier which acts as a low-cost redundant backup. Collage-CNN then provides backup classification results if a single image classification straggles. Deploying the Collage-CNN models in the cloud, we demonstrate that the 99th percentile tail latency of inference can be reduced by 1.47X compared to replication based approaches while providing high accuracy. Also, variation in inference latency can be reduced by 9X with a slight increase in average inference latency.
There is large amount of open source data sets available on the Internet for Machine Learning, but while managing your own project you may require your own data set. Today, let's discuss how can we prepare our own data set for Image Classification. The first and foremost task is to collect data (images). One can use camera for collecting images or download from Google Images (copyright images needs permission). There are many browser plugins for downloading images in bulk from Google Images.
This concludes our high level explanation of neural style transfer. We use a trained convolutional neural network (CNN) model such as VGG19 to acquire the content and style loss functions. Recall that content are high level features that describe objects and their arrangement in the image. An image classification model needs to be well-trained on content in order to accurately label an image as "dog" or "car". A convolutional neural network (CNN) is designed to filter out the high level features of an image.