Engine explained in this ppt ,takes a query image as an input do some process on it ,compare this image with images present in database and retrieve similar images. It uses the concept of content based image retrieval.
Lec14: Evaluation Framework for Medical Image SegmentationUlaş Bağcı
How to evaluate accuracy of image segmentation?
– Gold standard ~ surrogate of truths
– Qualitative • Visual
• Inter-andintra-observeragreementrates – Quantitative
• Volumetricmeasurements(regression) • Regionoverlaps
• Shapebasedmeasurements
• Theoreticalcomparisons
• STAPLE,Uncertaintyguidance,andevaluationw/otruths
Clustering – K-means – FCM (fuzzyc-means) – SMC (simple membership based clustering) – AP(affinity propagation) – FLAB(fuzzy locally adaptive Bayesian) – Spectral Clustering Methods ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
Introduction to digital image processing, image processing, digital image, analog image, formation of digital image, level of digital image processing, components of a digital image processing system, advantages of digital image processing, limitations of digital image processing, fields of digital image processing, ultrasound imaging, x-ray imaging, SEM, PET, TEM
Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Histogram equalization can be used to improve the visual appearance of an image. Peaks in the image histogram (indicating commonly used grey levels) are widened, while the valleys are compressed.
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)Ulaş Bağcı
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
Inside the ANN: A visual and intuitive journey to understand how artificial n...XavierArrufat
Presentation before the BCN Python Meetup Group aimed at explaining how Artificial Neural Networks work, with a purely graphical approach.
No math nor code is used during the presentations, only images and analogies. Gladly enough, the audience was somehow surprised they now have understand intuitively how these mathematical engines work. The objective was fully met.
Besides, the presentation compares how humans learn to read (by reiteration) and computers with ANN do (no surprise: by massive reiteration).
Engine explained in this ppt ,takes a query image as an input do some process on it ,compare this image with images present in database and retrieve similar images. It uses the concept of content based image retrieval.
Lec14: Evaluation Framework for Medical Image SegmentationUlaş Bağcı
How to evaluate accuracy of image segmentation?
– Gold standard ~ surrogate of truths
– Qualitative • Visual
• Inter-andintra-observeragreementrates – Quantitative
• Volumetricmeasurements(regression) • Regionoverlaps
• Shapebasedmeasurements
• Theoreticalcomparisons
• STAPLE,Uncertaintyguidance,andevaluationw/otruths
Clustering – K-means – FCM (fuzzyc-means) – SMC (simple membership based clustering) – AP(affinity propagation) – FLAB(fuzzy locally adaptive Bayesian) – Spectral Clustering Methods ShapeModeling – M-reps – Active Shape Models (ASM) – Oriented Active Shape Models (OASM) – Application in anatomy recognition and segmentation – Comparison of ASM and OASM ActiveContour(Snake) • LevelSet • Applications Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology Fuzzy Connectivity (FC) – Affinity functions • Absolute FC • Relative FC (and Iterative Relative FC) • Successful example applications of FC in medical imaging • Segmentation of Airway and Airway Walls using RFC based method Energy functional – Data and Smoothness terms • GraphCut – Min cut – Max Flow • ApplicationsinRadiologyImages
Image segmentation techniques
More information on this research can be found in:
Hussein, Rania, Frederic D. McKenzie. “Identifying Ambiguous Prostate Gland Contours from Histology Using Capsule Shape Information and Least Squares Curve Fitting.” The International Journal of Computer Assisted Radiology and Surgery ( IJCARS), Volume 2 Numbers 3-4, pp. 143-150, December 2007.
Prospects of Deep Learning in Medical ImagingGodswll Egegwu
A SEMINAR Presentation on the Prospects of Deep Learning in Medical Imaging Presented to the Department of Computer Science, Nasarawa State Polytechnic, Lafia.
BY:
EGEGWU, GODSWILL
08166643792
http://facebook.com/godswill.egegwu
http://egegwugodswill.name.ng
Explainable AI makes the algorithms to be transparent where they interpret, visualize, explain and integrate for fair, secure and trustworthy AI applications.
A presentation on Image Recognition, the basic definition and working of Image Recognition, Edge Detection, Neural Networks, use of Convolutional Neural Network in Image Recognition, Applications, Future Scope and Conclusion
Introduction to digital image processing, image processing, digital image, analog image, formation of digital image, level of digital image processing, components of a digital image processing system, advantages of digital image processing, limitations of digital image processing, fields of digital image processing, ultrasound imaging, x-ray imaging, SEM, PET, TEM
Histogram equalization is a method in image processing of contrast adjustment using the image's histogram. Histogram equalization can be used to improve the visual appearance of an image. Peaks in the image histogram (indicating commonly used grey levels) are widened, while the valleys are compressed.
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
Lec8: Medical Image Segmentation (II) (Region Growing/Merging)Ulaş Bağcı
. Region Growing algorithm
• Homogeneity Criteria
• Split/Merge algorithm
• Examples from CT, MRI, PET
• Limitations
Image Filtering, Enhancement, Noise Reduction, and Signal Processing • MedicalImageRegistration • MedicalImageSegmentation • MedicalImageVisualization • Machine Learning in Medical Imaging • Shape Modeling/Analysis of Medical Images Deep Learning in Radiology
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
Inside the ANN: A visual and intuitive journey to understand how artificial n...XavierArrufat
Presentation before the BCN Python Meetup Group aimed at explaining how Artificial Neural Networks work, with a purely graphical approach.
No math nor code is used during the presentations, only images and analogies. Gladly enough, the audience was somehow surprised they now have understand intuitively how these mathematical engines work. The objective was fully met.
Besides, the presentation compares how humans learn to read (by reiteration) and computers with ANN do (no surprise: by massive reiteration).
Continues with Excel basics giving information on cell addressing styles and worksheet functions and their nesting. Also gives an example of precision setting
We recently updated the Developmental Continua at GWA in Math and Literacy. Here is a parent presentation I did to help parents understand the new continua.
Keynote at Codebits in Portugal, April 2014, explaining the how and why of Firefox OS and how to use it.
Video: https://videos.sapo.pt/ZYQyY57ZlB6lhgIdBzrs
La historia se sitúa en Ciudad Academia, una ciudad tecnológicamente avanzada localizada al oeste de Tokio que se especializa en el desarrollo de poderes psíquicos, pero también se sitúa en un mundo donde la magia es real. Touma Kamijou es un estudiante de secundaria que posee en su mano derecha un misterioso poder llamado Imagine Breaker, el cual puede negar cualquier fenómeno sobrenatural ya sea psíquico o mágico, lo que también anula su propia buena suerte. Un día encuentra a una chica llamada Index colgada de su balcón. Ella es una monja que posee en su mente el Index Librorum Prohibitorum, el cual es una colección de 103,000 libros mágicos. Cuando los caminos de la ciencia y la magia se cruzan, esta historia comienza.
A NOVEL WEB IMAGE RE-RANKING APPROACH BASED ON QUERY SPECIFIC SEMANTIC SIGNAT...Journal For Research
Image re-ranking, is an effective way to improve the results of web-based image search. Given a query keyword, a pool of images are initailly retrieved primarily based on textual data, the remaining images are re-ranked based on their visual similarities with the query image corresponding to the user input. A major challenge is that the similarities of visual features don't well correlate with images’ semantic meanings that interpret users’ search intention. Recently people proposed to match pictures in a semantic space that used attributes or reference categories closely associated with the semantic meanings of images as basis. Even though, learning a universal visual semantic space to characterize extremely diverse images from the internet is troublesome and inefficient. In this thesis, we propose a completely distinctive image re-ranking framework that learns completely different semantic spaces for numerous query keywords automatically at the on-line stage. The visual features of images are projected into their corresponding semantic spaces to induce semantic signatures. At the online stage, images are re-ranked by scrutiny their semantic signatures obtained from the semantic spaces such that by the query keyword. The proposed query-specific semantic signatures considerably improve both the accuracy and efficiency of image re-ranking.
Novel Hybrid Approach to Visual Concept Detection Using Image AnnotationCSCJournals
Millions of images are being uploaded on the internet without proper description (tags) about these images. Image retrieval based on image tagging approach is much faster than Content Based Image Retrieval (CBIR) approach but requires an entire image collection to be manually annotated with proper tags. This requires a lot of human efforts and time, and hence not feasible for huge image collections. An efficient method is necessary for automatically tagging such a vast collection of images. We propose a novel image tagging method, which automatically tags any image with its concept. Our unique approach to solve this problem involves manual tagging of small exemplar image set and low-level feature extraction of all the images, hence called a hybrid approach. This approach can be used to tag a large image dataset from manually tagged small image dataset. The experiments are performed on Wang's Corel Dataset. In the comparative study, it is found that, the proposed concept detection system based on this novel tagging approach has much less time complexity of classification step, and results in significant improvement in accuracy as compared to the other tagging approaches found in the literature. This approach may be used as faster alternative to the typical Content Based Image Retrieval (CBIR) approach for domain specific applications.
A Comparative Study of Content Based Image Retrieval Trends and ApproachesCSCJournals
Content Based Image Retrieval (CBIR) is an important step in addressing image storage and management problems. Latest image technology improvements along with the Internet growth have led to a huge amount of digital multimedia during the recent decades. Various methods, algorithms and systems have been proposed to solve these problems. Such studies revealed the indexing and retrieval concepts, which have further evolved to Content-Based Image Retrieval. CBIR systems often analyze image content via the so-called low-level features for indexing and retrieval, such as color, texture and shape. In order to achieve significantly higher semantic performance, recent systems seek to combine low-level with high-level features that contain perceptual information for human. Purpose of this review is to identify the set of methods that have been used for CBR and also to discuss some of the key contributions in the current decade related to image retrieval and main challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. By making use of various CBIR approaches accurate, repeatable, quantitative data must be efficiently extracted in order to improve the retrieval accuracy of content-based image retrieval systems. In this paper, various approaches of CBIR and available algorithms are reviewed. Comparative results of various techniques are presented and their advantages, disadvantages and limitations are discussed.
A Survey on Content Based Image Retrieval SystemYogeshIJTSRD
The increasing increase of picture databases in practically every industry, including medical science, multimedia, geographic information systems, photography, journalism, and so on, necessitates the development of an effective and efficient approach for image processing. The approach of content based image retrieval is used to recover images based on their content, such as texture, colour, shape, and spatial layout. However, because to the semantic mismatch between the users high level notions and the images low level properties, retrieving the image is extremely challenging. Many concepts were presented in effort to close this gap. Furthermore, images can be stored and extracted depending on a variety of properties, one of which being texture. Content based Image Retrieval has become a popular study area as a result of the growth of video and image data in digital form. Digital data, such as criminal photographs, fingerprints, and scene photographs, has been widely used in forensic sciences. As a result, arranging such enormous amounts of visual data, such as how to quickly find an interesting image, becomes a major difficulty. There is a pressing need to develop an effective method for locating photographs. An image must be represented with particular features in order to be found. Three significant visual qualities of an image are colour, texture, and shape. The search for images utilising colour, texture, and shape attributes has gotten a lot of press. Preeti Sondhi | Umar Bashir "A Survey on Content Based Image Retrieval System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43777.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/43777/a-survey-on-content-based-image-retrieval-system/preeti-sondhi
Comparison of Various Web Image Re - Ranking TechniquesIJSRD
Image re-ranking, is an quite an efficient way to improve the results that are fetched from the web-based image search query. Given a query keyword for the image, a pool of images are first retrieved based on textual information, then the images are re-ranked based on their visual similarities with the query image according to the user input. But when, the images’ visual features do not match with the semantic meanings of the users’ entered query or keyword, it becomes a major challenge to make available the actual searched image. Hence, in this paper, the various Web image Re- ranking techniques are studied, on how it approaches towards the Web Image search that the user has input in query.
Evolving a Medical Image Similarity SearchSujit Pal
Slides for talk at Haystack Conference 2018. Covers evolution of an Image Similarity Search Proof of Concept built to identify similar medical images. Discusses various image vectorizing techniques that were considered in order to convert images into searchable entities, an evaluation strategy to rank these techniques, as well as various indexing strategies to allow searching for similar images at scale.
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...IJSRD
In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the users’ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.
A novel Image Retrieval System using an effective region based shape represen...CSCJournals
With recent improvements in methods for the acquisition and rendering of shapes, the need for retrieval of shapes from large repositories of shapes has gained prominence. A variety of methods have been proposed that enable the efficient querying of shape repositories for a desired shape or image. Many of these methods use a sample shape as a query and attempt to retrieve shapes from the database that have a similar shape. This paper introduces a novel and efficient shape matching approach for the automatic identification of real world objects. The identification process is applied on isolated objects and requires the segmentation of the image into separate objects, followed by the extraction of representative shape signatures and the similarity estimation of pairs of objects considering the information extracted from the segmentation process and shape signature. We compute a 1D shape signature function from a region shape and use it for region shape representation and retrieval through similarity estimation. The proposed region shape feature is much more efficient to compute than other region shape techniques invariant to image transformation.
Searching Images: Recent research at SouthamptonJonathon Hare
Knowledge Media Institute seminar series. The Open University. 23rd March 2011.
Southampton has a long history of research in the areas of multimedia information analysis. This talk will focus on some of the recent work we have been involved with in the area of image search. The talk will start by looking at how image content can be represented in ways analogous to textual information and how techniques developed for indexing text can be adapted to images. In particular, the talk will introduce ImageTerrier, a research platform for image retrieval that is built around the University of Glasgow's Terrier text retrieval software. The talk will also cover some of our recent work on image classification and image search result diversification.
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
1. Image Search: Then and Now
Integrated Knowledge Solutions
iksinc@yahoo.com
sikrishan@gmail.com
iksinc.wordpress.com
2. Outline
• Introduction
• Image = Content + Context
• Content Based Image Retrieval (CBIR)
• Bridging the Semantic Gap
• Using Social Interactions for Retrieval
• Where do we go from here
3. What is Image Search?
• Image search means retrieving images from an
image database that satisfy the user’s need.
• The user need may be expressed in the following
ways:
– Keywords or text describing the image content
– An exemplar image
• Other names for image search
– Image retrieval
– Image similarity search
– Content based image retrieval (CBIR)
5. Nalanda University was one of the first universities
in the world, founded in the 5th Century BC, and
reported to have been visited by the Buddha during
his lifetime. At its peak, in the 7th century AD,
Nalanda held some 10,000 students when it was
visited by the Chinese scholar Xuanzang.
6. The Royal Library of Alexandria, in Egypt, seems to have been
the largest and most significant great library of the ancient
world. It functioned as a major center of scholarship from its
construction in the third century B.C. until the Roman
conquest of Egypt in 48 B.C.
8. But Now a Days
No Distinction Between Document Producers
and Consumers
9.
10. Some Relevant Numbers
Flickr has over 6 billion pictures as of August 2011,
and 3.5 million images are uploaded daily.
Photobucket has more than 10 Billion images, and
over 4 million images are uploaded everyday.
Facebook has over 60 Billion photos and more than
350 million photos are uploaded everyday.
Instagram has over 20 billion photos. About 60
million photos are uploaded everyday.
11. An image now a days is not just a
picture but it is a picture with
thousand words
12. Image = Content + Context
Tags
Cherry
blossom
Japantown
San
Francisco
Peace
Pagoda
Content Context
13. So, image retrieval should benefit
from the contextual component, if
present.
How?
But, first let us look at image
retrieval from the content
perspective only
15. A Typical QBIC Type Image Retrieval
System
Feature
Extraction
FeaturesMedia
Collection
Indexing &
Matching
Query Feature
Extraction
Retrieved
Results
Relevance
Feedback
Such systems/approaches are often referred to as Content
Based Image Retrieval (CBIR)
16.
17.
18. Semantic Gap
Early systems produced results
wherein the retrieved
documents were visually similar
(signal level similar) but not
necessarily similar in showing
the same semantic concept.
Content-Based Image Retrieval at the End of the Early Years,
IEEE Transactions on Pattern Analysis and Machine Intelligence , Arnold
Smeulders , Marcel Worring , Simone Santini , Amarnath Gupta ,
Ramesh Jain , December 2000
http://www.searchenginejournal.com/7-similarity-based-image-search-engines/8265/
19. Semantic Gap
Users also like to query using descriptive
words rather than query images or other
multimedia objects. This requires retrieval
systems to correlate low-level features
with high level concepts.
Visually dissimilar
images representing
the same concept.
21. How to Bridge the Semantic Gap?
Manual annotation
Use machine learning to:
• Build image category classifiers to
perform semantic filtering of the
results
• Build specific detectors for objects to
associate concepts with images
•Build object models using low level
features
Exploit context:
• Text surrounding images
• Associated sound track and
closed captions in videos
• Query history
27. Example of Image Search using Keywords
Search result in 2014
Again, the results are better organized in sub-
categories
28. Exploiting Context: An Example
Kulesh, Petrushin and Sethi, “The PERSEUS Project: Creating Personalized Multimedia News Portal,”
Proceedings Second Int’l Workshop on Multimedia Data Mining, 2001
29. Machine Learning of Image Concepts
• Challenging problem
• Presence of multiple concepts/multiple instances
• Disproportionate number of negative examples
• Manpower need for labeling training examples
30. Feature Extraction Issues
Whole image based features.
Easy to use but not very
effective
Region based features. Both
regular region structure and
segmented regions are popular
Salient objects based features.
Connected regions
corresponding to dominant
visual properties of objects in an
image
31. Scale Invariant Feature Transform
(SIFT) Descriptors
SIFT descriptors or its variants are
currently the most popular features
in use. Each image generates
thousands of features (key point
descriptors) with each feature
typically consisting of 128 values
http://www.vlfeat.org/
D. G. Lowe, “Distinctive image
features from scale-invariant
keypoints,” IJCV, 2004.
32. Learning Image Concepts
• Both supervised and unsupervised
learning methods (SVM, DT, AdaBoost,
VQ etc.) have been used
• Early work limited to few tens of
categories; however some of the current
systems can work with thousands of
categories/concepts
33. VQ Based Learning Classifier
Test
Image
Best
Codebook
Label
Water Codebook
Sky Codebook
Fire Codebook
Mustafa & Sethi (2004)
36. Co-occurrence of Bag of Words
Image
Collection
Edge
Analysis
Images
Collection of
Binary Image
Blocks
Clustering
Local
Feature
Descriptors
(Codewords)
Codeword
Representation
Of Images
Co-occurrence
Matrices of
Local Features
Compute
Distances
Image
Distance
Matrix
Pathfinder
Network
Mukhopadhyay, Ma, and Sethi, “Pathfinder Networks for Content Based Image
Retrieval Based on Automated Shape Feature Discovery,” ISMSE 2004
37. Co-occurrence of BoW
Original image
Representation by
feature indices
(cluster membership)
Co-occurrence matrix
)},(),,(max{),( ABhBAhBAH
))max(min(),( AaBbbaBAh
Hausdorff metric
Manhattan distance
43. IMARS provides a large number of built-in classifiers for visual categories that cover places, people, objects, settings,
activities and events. It is easy to add new ones. IMARS can work on PC or laptop (trial version is available at IBM
alphaWorks). IMARS can also work at large-scale for high-volume batch processing of millions and images and videos
per day. Several demos of IMARS are available (see IMARS demos)
Image Category Classifiers Examples
44. Semantic labeling. (a) An MPE semantic retrieval system groups images by semantic
concept and learns a probabilistic model for each concept. (b) The system represents
each image by a vector of posterior concept probabilities.
From Pixels to Semantic Spaces: Advances in Content-Based Image
Retrieval (Nuno Vasconcelos, IEEE Computer, July 2007)
Image Classification via Probabilistic
Modeling
45. Image = Content + Context
Tags
Cherry
blossom
Japantown
San
Francisco
Peace
Pagoda
Content Context
47. About Tags
• User centered
• Imprecise and often overly personalized
• Tag distribution follows power law
• Most users use very few distinct tags while a small group of users works
with extremely large set of tags
• Also known as Folksonomy, social tagging, and social classification
48. Why Not Use Social Tags for Retrieval?
Problem: The relevant tag is
often not at the top of the list.
Only less than 10% of the
images have their most relevant
tag at the top of the list.
Solution: Improve tagging by
suggesting potential tags to a
user / tag ranking /tag
completion etc.
Dong Liu, Xian-Sheng Hua, Linjun Yang, Meng Wang, Hong-
Jiang Zhang. Tag Ranking. WWW 2009. Madrid, Spain
49. Tag Recommendation using Tags
Co-occurrences
Given a target image and initial tags, use co-occurrence of tags to
recommend tags for the target image. This approach doesn’t take into
account the visual features co-occurrences.
50. Tag Recommendation using Tags
Co-occurrences and Visual Similarity
Kucuktunc, Sevil, Tosun, Zitouni, Duygulu, and Can (SAMT 08)
Given a target image and initial tags, use the existing tagged images to
suggest tags for the target image.
55. Tag Recommendation After Tag
Ranking
• Given an untagged image, find its visually similar “k” images
• Pool the top two ranked tags from k images and select the unique tags as
recommended tags
56. Tag Completion
The complete tag matrix is
generated by imposing
constraints based on visual
similarity, tag to tag similarity,
and similarity with the initial
tag matrix. The matrix
completion is done by an
optimization procedure.
Wu and Jain, IEEE-PAMI, JANUARY
2011
57. What about Taggers & Commenters?
Question: How can we incorporate taggers/commenters
characteristics for improved tag recommendations?
Answer: Use three sets of features: derived from image to
be tagged, user’s tag history, and user’s social interactions
58. Tag History & Social Interaction
Features
Tag history features are based
on the tags the user has used
in the past
Social interaction features are
derived from tags/comments
posted by the user’s
friends/favorite posters
X. Chen & H. Shin, ICDM 2010
59. Current Status of Image Search
• Extensive interest as evident from conferences, journals, and
special issues
• Overall, solid progress is being made
• Efforts towards performance evaluation with benchmarked
collections are gaining more traction
• Integration of content and context through tags and
comments is receiving increasing attention to help improve
retrieval
• Killer applications are beginning to emerge as visual search
gains prominence
• Need for more applications outside entertainment
60. Performance Evaluation Efforts
ImageCLEF2013
- Annotation Task:
- 250000 Training Images
- 95 (develop), 116 (test) concepts to be identified
- A lot of label Noise inside the training set, due to the automatic label
extraction from websites
61. Performance Evaluation Efforts
TRECVID workshops, an offshoot of TREC, are yearly evaluation meetings since
2003. The goal of the workshops is to encourage research in content-based
video retrieval and analysis by providing large test collections, realistic system
tasks, uniform scoring procedures, and a forum for organizations interested in
comparing their results.
63. CBIR for Whole Slide Imageries
• The availability of digital whole slide data sets
represent an enormous opportunity to carry out
new forms of numerical and data- driven query,
in modes not based on textual, ontological or
lexical matching.
– Search image repositories with whole images or
image regions of interest
– Carry our search in real-time via use of scalable
computational architectures
Extraction from Image
repositories based upon
spatial information
Analysis of data
in the digital domain
…001011010111010111..
Resultant Surface Map or
gallery of matching images
or
Slide courtesy of Ulysses J. Balis, M.D.
Director, Division of Pathology Informatics
Department of Pathology
University of Michigan Health System
64. Medical Image Retrieval
Text
“Find all the cases in which a tumor decrease in size
for less than three month post treatment, then
resumed a growth pattern after that period”
QUERY ?
Text + medical image
“Find images with large-sized frontal lobes brain tumors for
patients approximately 35 years old”
+Medical image
QUERY IMAGE-BASED CONCEPTS
Medical image ij - Specific Signature
ImageiQuery
VB-Spec CUIp
VB-Gen CUI1
VB-Spec CUIkIMAGE-BASED
ONTOLOGY
GENRAL AND SPECIALIZED
QUERY MEDICAL IMAGE
VISUAL ANALYSIS
Text query
CUIn
CUI1
CUI2
QUERY TEXT-BASED CONCEPTS
Textual query i - Indexes
MEDICAL
ONTOLOGY
TEXT QUERY
CONCEPTS
EXTRACTION
71. Take Home Message
• Image/video retrieval is moving in the
commercial domain. Lot more activity is expected
in near future
• Multimodal/cross-modal retrieval is gaining
importance
• Approaches combining social search and visual
search techniques are expected to gain
prominence
• Crowdsourcing is a cheap and effective way of
tagging media
72. Acknowledgement
• This presentation is based on the work of
numerous researchers from the MIR/ML/CVPR
community. I have tried to give
credit/references wherever possible. Any
omission is unintentional and I apologize for
that.
• Also want to thank my present and past
students and collaborators.