Image Recognition Using Deep Learning
This step to recognize image is pretty easy. The image data, both training, and test are organized. Training data is different from test data, which also means we remove duplicates (or near duplicates) between them. This data is fed into the model to recognize images. We have to find the image of a cat in our database of known images which has the closest measurements to our test image. All we need to do is train a classifier that can take the measurements from a new test image and tells us about the closest match with a cat. Running this classifier takes milliseconds. 
The result of the classifier is the ‘cat’ or ‘non-cat’. The major challenges in building an image recognition model are hardware processing power and cleansing of input data. It can be possible that most of the images might be high definition. If you are dealing with large images of size more than 500 pixels, it becomes 250,000 pixels (500 x 500) per image. A training data of mere 1000 images will amount to 0.25 billion values for the machine learning model. Moreover, the calculations are not easy addition or multiplication, but complex derivatives involving floating point weights and matrices. There are some quick hacks to overcome the above challenges: – image compression tools to reduce image size without losing clarity – use grayscale and gradient version of colored images – graphic processor units (gpu) – to train the neural networks containing large data sets in less time and with less computing infrastructure.
Broadly speaking, visual search is the process of using real-world images to produce more reliable, accurate online searches. visual search allows retailers to suggest items that thematically, stylistically, or otherwise relate to a given shopper’s behaviors and interests. Using a deep learning approach to image recognition allows retailers to more efficiently understand the content and context of these images, thus allowing for the return of highly-personalized and responsive lists of related results. These techniques are already being used by major retailers (ebay, asos, neimann marcus), tech giants (google lens), and social media companies (pinterest lens), and though these approaches are still (relatively speaking) in their infancy, the results are compelling : 55% of consumers say visual search is instrumental in developing their style and taste. The “global visual search market” is estimated to surpass $14.7 billion by 2023. When shopping online for clothing or furniture, more than 85% of respondents respectively put more weight on visual info than text info.
This article aims at training machines to recognize images similarly as people do. Image recognition belongs to the group of supervised learning problems, i.e., classification problems, to be more precise. This article presents a relatively simple approach of training a neural network to recognize digits. This approach uses an ordinary feedforward neural network. The accuracy of the model can be further improved using other techniques.
Developing an image recognition solution that is based on a new set of features must be compared against a strong baseline of well established feature extraction techniques. As a benchmark we apply a set of classical descriptors that are known in the literature to perform well in image classification/categorization tasks. These include gist features, pyramid histogram of oriented gradients (PHOG) , gabor , and gray-level co-occurrence matrix (GLCM) statistics. We also compare the deep feature representation to the more recent state-of-the-art representation of the bag-of-visual-words (BOVW) model.
IBM provides the Watson Visual Recognition service on the ibm cloud which relies on deep learning algorithms for analyzing images for scenes, objects, and other content. Users can build, train, and test custom models within or outside of the watson studio. The demo of a custom model by vehicle glass repair company belron. Source: IBM another feature available in beta enables users to train object detection models. Pre-trained models include: general model – provides default classification from thousands of classes explicit model – defines whether an image is inappropriate for general use food model – identifies food items in images text model – extracts text from natural scene images. Also, developers can include custom models in ios apps with core ml APIs and work in a cloud collaborative environment with notebooks in watson studio. Pricing. IBM offers two pricing plans – lite and standard. Lite: users can analyze 1,000 images per month with custom and pre-trained models for free and create and retrain two free custom models. The provider also offers core ML exports as a special promotional offer. Standard: image classification and custom image classification costs $0.002 per image and training a custom model costs $0.10 per image. Free core ML exports are also included in the plan.
Certain types of ai are going to transform your career in marketing and sales sooner than you think. We talked about how voice recognition is going to completely alter the search landscape for marketers. Image recognition is another AI technology that is going to impact your work and career, too. Image recognition is the ability of a machine to process and analyze the content of photos. If an image recognition system is able to accurately analyze what it sees, it opens up a whole world of possibilities—because the technology is versatile. Image recognition powers facebook’s ability to recognize you or people you know in photos. It’s also used by content providers to identify inappropriate content in images. And it’s even able to analyze x-rays as well as doctors. This adaptable tech is also going to change how brands do business and marketers do their jobs. Why? Because, chances are, you use or rely on a ton of images in your marketing—or you market to buyers on platforms that rely heavily on visuals. When image recognition is turned loose on these visuals, it can offer marketers some serious insights into what consumers share, buy, and click. By analyzing the millions and billions of visuals that people share everyday, machines are, in fact, able to make your marketing far more intelligent and far more human. note: the analysis of video or real-time environments is called computer vision. It uses the same principles of image recognition, but has its own set of massive implications for professionals—so we’re going to dedicate an entire post to it in the near future. How image recognition works: a 2-minute explainer most marketers don’t need to know the technical complexities of image recognition to actually take advantage of it. We want you to get started with AI as fast as possible, so, instead, we’ve created a two-minute explainer to teach you what image recognition is at a high level. Ready? let’s go. Image recognition is an AI-powered technology that understands the content of photos. AI is an umbrella term for a series of related technologies that use large datasets to make predictions. In this case, image recognition predicts what it’s seeing in a visual—and these days, it’s usually right. (Image recognition systems have become very accurate since breakthroughs in 2012 …) when image recognition “sees” an image, a complex series of algorithms trained to recognize certain patterns analyzes the image at the individual pixel level. The image recognition system has been trained on large numbers of images (thousands, millions or billions), so it knows, with a high degree of accuracy, what objects actually are. The image recognition system takes that trained knowledge, then applies it to new images. Then, it predicts what object it is seeing in the image. Additional machine learning is then used to analyze the outputs of the image recognition system, offering insights into the sets of images you give it. Why does this matter for marketers? Because image recognition systems can identify what’s in a single image. But with additional machine learning capabilities, they can then extract insights from thousands, millions, or even billions of images. What does image recognition mean for your marketing? image recognition is here, and it’s used across countless platforms you interact with everyday. Social media alone is going to change thanks to image recognition. The major platforms already use it to improve the user experience. But these platforms are enormous image repositories, too. When you have this much image data available, AI-powered technologies like image recognition might be able to work wonders. In fact, tools exist today that analyze images on social media and across the internet, then extract insights from those images. These insights can tell you a lot about consumers, like what brands they share or what content resonates with them. This affects how brands market to consumers, where marketers run campaigns, and even what products your business may want to create. These insights can even inform how you create ads and social media posts, since AI-powered image recognition can tell you which images and visuals produce the best results. Basically, if you rely on visual social or advertising to drive business, you should be looking into image recognition. This is all about competitive and career advantage. Right now, a lot of social media and advertising creative is crafted based on gut. It’s subjective, created based on a sense of what looks and feels right, then gets approved by a team. It may, in an ideal world, be informed by customer research and digital marketing data. But no human has the ability to analyze billions of images online, then extract insights about why certain images get clicked on, viewed, or engaged with more than others. It’s just not possible. That’s because effective human-driven image analysis is functionally impossible in an age with more than one billion instagram accounts alone are active each month. There’s just too much data, and marketers are drowning in it. With the data, comes expectations. Brands are expected to increasingly personalize and target their offerings. Marketers are expected to run data-driven campaigns that perform. And visual content is a key component of most modern campaigns. Artificial intelligence isn’t just fun to read about. Brands that rely on visuals actually need it to survive and thrive. We often talk about the business use case for AI as one of either cost reduction or revenue enhancement. In this case, it’s both. Image recognition and related AI technologies have the ability to save brands tons of wasted dollars on social and advertising, by pinpointing what actually works and what doesn’t, backed with datasets big enough to matter. They also have the ability to dramatically increase revenue. The billions of online images at your fingertips represent a goldmine of data, just ready to be mined for insights into how your prospects and customers buy. Image recognition analysis of physical goods in stores is also producing in-person data on how people buy. Social and ad campaigns powered by these insights have the potential to turn every dollar you spend into a money tree. This is just the beginning. Brands need to start understanding what’s possible. What does image recognition mean for your career? as a professional marketer, artificial intelligence in some form is going to transform your career. Image recognition, specifically, could have serious implications for social media marketers and advertising professionals. We don’t see image recognition replacing what social media marketers and ad pros do everyday. Like we mentioned, image recognition actually does things no human can. But it will change the game for these marketers and force them to level up. First, it will require these marketers to become familiar with artificial intelligence. As brands learn what’s possible with image recognition, we expect executives to demand that teams plan for and pilot AI-powered technologies. Those that fail to adapt risk becoming irrelevant when stacked up against teams empowered by AI. Make no mistake: talented social and advertising professionals will still have the core competencies needed to succeed. But AI provides such a competitive advantage that they’ll be unable to keep up with even less skilled professionals armed with the technology. Second, we expect the market will require social media and advertising professionals to get even more creative than they are today. Right now, these pros straddle the line between analyst and artist, collecting some data on what works and using it to inform creative. But, with technologies like image recognition, the data part is going to be completely outsourced to a machine. Your AI coworker will show you what works best with which audiences. It will be your job to take those insights and build incredible creative work that actually resonates with audiences. In today’s world, you can get by with good targeting and decent creative. In tomorrow’s world, where everyone is armed with AI-driven targeting and insights, your ability to make consumers feel something profound is going to be in even higher demand than it is today. Finally, you’re going to have to adapt, no matter what comes your way. The reality is, you’re probably not just going to be a social media or advertising specialist anymore in an age of AI. AI is going to automate and augment the daily tasks that marketers do. If audience targeting or analysis is a big part of your job, AI probably will do it better. That means your job will still exist, but it’ll change. You’ll have to find other ways to create value—and they may be outside whatever traditional job description you have internally. In the end, this isn’t meant to scare you. But it is meant to motivate you. The marketing industry is on the cusp of a profound transformation. With transformation comes both creative destruction and serious opportunity. The good news? It’s still early days—and you have the tools available to start understanding where this is all going, so you can build a competitive advantage in your company and career.
Image recognition, in the context of machine vision, is the ability of software to identify objects , places, people, writing and actions in images. computers can use machine vision technologies in combination with a camera and artificial intelligence software to achieve image recognition. Image recognition is used to perform a large number of machine-based visual tasks, such as labeling the content of images with meta-tags , performing image content search and guiding autonomous robots, self-driving cars and accident avoidance systems. While human and animal brains recognize objects with ease, computers have difficulty with the task. Software for image recognition requires deep machine learning. Performance is best on convolutional neural net processors as the specific task otherwise requires massive amounts of power for its compute-intensive nature. Image recognition algorithms can function by use of comparative 3D models , appearances from different angles using edge detection or by components. Image recognition algorithms are often trained on millions of pre-labeled pictures with guided computer learning. current and future applications of image recognition include smart photo libraries, targeted advertising, the interactivity of media, accessibility for the visually impaired and enhanced research capabilities. Google , Facebook , Microsoft , Apple and Pinterest are among the many companies that are investing significant resources and research into image recognition and related applications. Privacy concerns over image recognition and similar technologies are controversial as these companies can pull a large volume of data from user photos uploaded to their social media platforms.
Data Augmentation using learned transformations for One-Shot Medical Image Segmentation
Labeling medical images requires significant expertise and time, and
typical hand-tuned approaches for data augmentation fail to capture the complex
variations in such images. We present an automated data augmentation method for synthesizing labeled medical images. We demonstrate our method on the task of segmenting magnetic resonance imaging (MRI) brain scans. Our method requires only a single
segmented scan, and leverages other unlabeled scans in a semi-supervised
approach. We learn a model of transformations from the images, and use the
model along with the labeled example to synthesize additional labeled examples.
each transformation is comprised of a spatial deformation field and an
intensity change, enabling the synthesis of complex effects such as variations
in anatomy and image acquisition procedures. We show that training a supervised
segmenter with these new examples provides significant improvements over
state-of-the-art methods for one-shot biomedical image segmentation. Our examples
are available here.
Go to a specific data
The process of determining relevant image features is often complicated by
contradictory tensions at work when images are viewed for diagnostic purposes.
A duality arises from the simultaneous but cognitively separable processes in
which a global gestalt diagnostic impression is formed simultaneously with an
awareness of evidentiary sub-element features. For example, a diagnostic
conclusion drawn from an image is often greater than, and not merely a result
of, an assemblage of small decisions about the existence of particular
elemental features (e.g.
, congestive heart failure diagnosis on X-ray is not a
deterministic conclusion from the presence of an enlarged heart and vascular
prominence). Thus, diagnostic classifications may be distinct from
explanations rationalized from the sum of anatomic features identifiable on an
image. Hence, retrieval of groups of images sharing a common feature but
perhaps not the same diagnostic classification can be motivated by the intent
to better understand the expression of disease. The computational tools
applicable to visually perceptible features commonly rest on histograms of
hue, saturation and intensity, texture measurements, and edge orientation, as
well as on object shape calculated over the whole or some designated local
area of an image. Digital networks have begun to support access to widely distributed sources of medical images as well as related clinical, educational, and research
information. The information, however, is voluminous, heterogeneous, dynamic,
and geographically distributed. This heterogeneity and geographic spread
create a demand for an efficient picture archiving system, but they also
generate a rationale for effective image database systems. Without development of the latter, the former would act as a means of communication but would not produce significant new medical knowledge. Picture collections remain an unresolved challenge except for those special class of images adaptable to geographic information systems (GIS), in which conventional geometry and verifiable ground truth are available.
In medicine to date, virtually all picture archive and communication
systems (PACS) retrieve images simply by indices based on patient name,
technique, or some observer-coded text of diagnostic findings. Using conventional database architecture, a user might begin with an image archive (an unorganized collection of images pertaining to a medical
theme e.g. a collection of magnetic resonance cardiac images) and some
idea of the type of information needed to be extracted. Fields of text tags,
such as patient demographics (age, sex, etc.), diagnostic codes (ICD-9,
american college of radiology diagnostic codes, etc.), image view-plane
(saggital, coronal, etc.), and so on usually are the first handles on this
process. There are a number of uses for medical image databases, each of which
would make different requirements on database organization. For example, an
image database designed for teaching might be organized differently than a
database designed for clinical investigation. classification of images into
named (e.g. hypernephroma, pulmonary atelectasis, etc.) or coded diagnostic
categories (e.g. ICD-9) may suffice for retrieving groups of images for
teaching purposes. in the case of text databases, tables of semantic
equivalents, such as can be found in a metathesaurus, permit mapping of
queries onto specific conventional data fields. Textual descriptors, however,
remain imprecise markers that do not intrinsically lend themselves to
calculable graded properties. For example, thesaurus entries commonly imply
related but nonsynonymic properties, as seen in the terms used to describe
variant shapes of the aorta: tortuous, ectatic, deformed, dilated, bulbous,
prominent. This textual approach, however, fails to fully account for quantitative and
shape relationships of medically relevant structures within an image that are
visible to a trained observer but not codable in conventional database terms.
the development of suitable database structures addressing the visual/spatial
properties of medical images have lagged. More effective management of the now
rapidly emerging large digital image collections motivates a need for
development of database methods that incorporate the relationship of
diagnostically relevant object shape and geometric
properties. However, unlike maps, whose conventional geometric properties make them
suitable for graphic information systems, the concepts of content and objects
relevant to medical image databases must accommodate the heterogeneity,
imprecision, and evolving nature of medical knowledge.
recently, some investigators have proposed image database structures
organized by certain properties of content. Most of these techniques are devoted to indexing large conventional collections of photographic images for the purpose of open-ended browsing or fixed objects found in industrial parts. For example, the query by image content (QBIC) system rests on color histogram extraction. This permits queries based on color percentages, color distribution, and textures. automatic color abstraction has advantages because it is easily segmented, but the approach would only be applicable to medical images based on light photography (e.g. dermatology), where color is an
inherent feature. Moreover, lacking reasoning procedures, all other metadata
is left untapped and unreachable. if the abstraction does not address the
image property the query is suited for, the search is hopeless, and retrieval
of an complete set of appropriate images residing in the collection is
unlikely. Requirements for medical image databases, however, differ substantially
from those applicable to general commercial image collections (commonly
referred to as "stock house" photo collections). To appreciate the
difference, we can categorize databases along three dimensions: The extent
to which the database schema can understand and reason about its content. We
will call this the "content understanding" axis. The ease with
which the database query mechanism allows the user to specify what the user
wants. If the database does not allow easy and intuitive translation of users'
common queries, then it cannot guarantee all relevant data have been retrieved
from the database. This can be called the "query completion" axis.
In addition, there is the extent of interaction required by an image
librarian at data entry or by the end user at retrieval. We will call this the
"user interaction axis."
