Image search engine www.immenselab.com is presented with its developing functionality that can be used for versatile search of images which are identical and similar to a pattern.
Frequently asked questions about using image search engine Immenselab that enables users to search for identical or similar images using a pattern. A test index database of 10 million images is used by www.immenselab.com to show how the engine works. It allows to use different search methods and to control search results on the fly.
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.
This document discusses building knowledge graphs using DIG (Distributed Information Graphs) to integrate heterogeneous data sources. It describes the steps involved, including data acquisition, feature extraction, mapping to an ontology, entity resolution, graph construction, and deployment. As a use case, DIG has been used to build a knowledge graph from over 100 million web pages related to human trafficking to help law enforcement identify victims and prosecute traffickers.
A DARPA Project named Memex crawls the Deep web looking for content to index for law enforcement use. Their advanced algorithms are designed to by pass membership areas and pay walls as well as avoid detection by system administrators. Learn more:
http://christopher.killerpenguin.net/blog/darpaprojectmemexerodesprivacy
- Introduction of CBIR and its evolution from early days to current era of deep learning was discussed.
- Three main stages of CBIR evolution were covered: early days before 2000 focused on hand-crafted features; days of bag-of-features model from 2000-2012 where local invariant features and visual codebooks were extensively studied; and current era of deep learning after 2012 where features are learned from data using deep neural networks.
- Key challenges and approaches at each stage like relevance feedback, local features, codebook creation, and use of pre-trained deep models were summarized.
The document provides information about a Proposer's Day event for the DARPA SHIELD program. The event included presentations on threats to the electronics supply chain from counterfeit parts, an overview of the SHIELD program goals to address these threats, and instructions for submitting abstracts and full proposals. Attendees were encouraged to form teams to develop comprehensive solutions and submit proposals by the deadlines of March 31 for abstracts and May 30 for full proposals. The event aimed to solicit innovative ideas and technologies to securely track hardware components through the global electronics supply chain.
This presentation summarizes a vertical image search engine that integrates text and visual features to improve image retrieval performance. The system architecture includes a crawler, preprocessor, and search interface. It represents keywords in visual feature space, weights visual features based on their relevance to keywords, and generates a visual thesaurus. The algorithm optimizes weight vectors, analyzes feature quality, and expands queries during search. Key modules are the user interface, parser, image processor, and crawler. In conclusion, combining text and visual features allows the system to select meaningful features that reflect user intentions for effective vertical search.
Frequently asked questions about using image search engine Immenselab that enables users to search for identical or similar images using a pattern. A test index database of 10 million images is used by www.immenselab.com to show how the engine works. It allows to use different search methods and to control search results on the fly.
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.
This document discusses building knowledge graphs using DIG (Distributed Information Graphs) to integrate heterogeneous data sources. It describes the steps involved, including data acquisition, feature extraction, mapping to an ontology, entity resolution, graph construction, and deployment. As a use case, DIG has been used to build a knowledge graph from over 100 million web pages related to human trafficking to help law enforcement identify victims and prosecute traffickers.
A DARPA Project named Memex crawls the Deep web looking for content to index for law enforcement use. Their advanced algorithms are designed to by pass membership areas and pay walls as well as avoid detection by system administrators. Learn more:
http://christopher.killerpenguin.net/blog/darpaprojectmemexerodesprivacy
- Introduction of CBIR and its evolution from early days to current era of deep learning was discussed.
- Three main stages of CBIR evolution were covered: early days before 2000 focused on hand-crafted features; days of bag-of-features model from 2000-2012 where local invariant features and visual codebooks were extensively studied; and current era of deep learning after 2012 where features are learned from data using deep neural networks.
- Key challenges and approaches at each stage like relevance feedback, local features, codebook creation, and use of pre-trained deep models were summarized.
The document provides information about a Proposer's Day event for the DARPA SHIELD program. The event included presentations on threats to the electronics supply chain from counterfeit parts, an overview of the SHIELD program goals to address these threats, and instructions for submitting abstracts and full proposals. Attendees were encouraged to form teams to develop comprehensive solutions and submit proposals by the deadlines of March 31 for abstracts and May 30 for full proposals. The event aimed to solicit innovative ideas and technologies to securely track hardware components through the global electronics supply chain.
This presentation summarizes a vertical image search engine that integrates text and visual features to improve image retrieval performance. The system architecture includes a crawler, preprocessor, and search interface. It represents keywords in visual feature space, weights visual features based on their relevance to keywords, and generates a visual thesaurus. The algorithm optimizes weight vectors, analyzes feature quality, and expands queries during search. Key modules are the user interface, parser, image processor, and crawler. In conclusion, combining text and visual features allows the system to select meaningful features that reflect user intentions for effective vertical search.
The document discusses the open-source "tetrahedron" which is made up of four key elements - community, code, and infrastructure (testing and sharing). It provides guidance on growing and engaging a community, maintaining high-quality code, and leveraging continuous integration and artifact sharing to test code and disseminate the project.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-opencv
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Gary Bradski, President and CEO of the OpenCV Foundation, presents the "OpenCV Open Source Computer Vision Library: Latest Developments" tutorial at the May 2015 Embedded Vision Summit.
OpenCV is an enormously popular open source computer vision library, with over 9 million downloads. Originally used mainly for research and prototyping, in recent years OpenCV has increasingly been used in deployed products on a wide range of platforms from cloud to mobile.
The latest version, OpenCV 3.0 is currently in beta, and is a major overhaul, bringing OpenCV up to modern C++ standards and incorporating expanded support for 3D vision. The new release also introduces a modular “contrib” facility that enables independently developed modules to be quickly integrated with OpenCV as needed, providing a flexible mechanism to allow developers to experiment with new techniques before they are officially integrated into the library.
In this talk, Gary Bradski, head of the OpenCV Foundation, provides an insider’s perspective on the new version of OpenCV and how developers can utilize it to maximum advantage for vision research, prototyping, and product development.
COM2304: Introduction to Computer Vision & Image Processing Hemantha Kulathilake
At the end of this lesson, you should be able to;
Describe image.
Describe digital image processing and computer vision.
Compare and Contrast image processing and computer vision.
Describe image sources.
Identify the array of imaging application under the EM Image source.
Describe the components of Image processing system and computer vision system.
OpenCV is an open-source library for computer vision and machine learning. The document discusses OpenCV's features including its modular structure, common computer vision algorithms like Canny edge detection, Hough transform, and cascade classifiers. Code examples are provided to demonstrate how to implement these algorithms using OpenCV functions and data types.
A search engine uses automated software programs called spiders that crawl the web to index pages and create a searchable database. When a user searches for keywords, the search engine software returns relevant results from the index. There are three main types of search engines - directories that are compiled by humans, hybrid engines that combine human and automated results, and meta search engines that search multiple other engines at once. Each search engine indexes pages differently and has a unique algorithm to determine search results.
The document discusses optimizing computer vision applications for cross-platform use. It describes conflicting requirements around being cross-platform versus utilizing specific device capabilities. Possible solutions discussed include optimizing for ARM NEON, a single platform, or all platforms. The document then introduces FastCV, a cross-platform computer vision library from Qualcomm that provides optimized implementations for different processors like Snapdragon to gain performance benefits while supporting multiple platforms.
Computer vision is a field that uses techniques to electronically perceive and understand images. It involves acquiring, processing, analyzing and understanding images and can take forms like video sequences. Computer vision aims to duplicate human vision abilities through artificial systems. It has applications in areas like manufacturing inspection, medical imaging, robotics, traffic monitoring and more. Some techniques used in computer vision include image acquisition, preprocessing, feature extraction, detection, recognition and interpretation.
This document discusses advances in image search and retrieval. It begins with an overview of visual information retrieval and its challenges, including the semantic gap between low-level visual features and high-level semantics. It then covers recent techniques like Google image search and similarity search. The document outlines core concepts like capturing similarity, large datasets, and user needs. It also revisits a 2000 paper on the challenges still facing the field, including the unsolved semantic gap and need for standardized evaluation benchmarks.
The document discusses different types of search engines. It describes search engines as programs that use keywords to search websites and return relevant results. It provides examples of popular search engines like Google, Yahoo, and Ask.com. It also explains different types of search engines such as crawler-based, directory-based, specialty, hybrid, and meta search engines. Finally, it discusses how to effectively use search engines through techniques like being specific, using symbols like + and -, and using Boolean searches.
The document discusses search engines, including how they work, their importance, and different types. It explains that search engines use crawlers to scan websites, extract keywords, and build databases. When users search, the engine returns relevant pages. Directories rely on human editors while hybrid engines use both crawlers and directories. Meta search engines transmit keywords to multiple engines and integrate results. Making effective searches involves keeping queries simple and considering how target pages may be described.
Scotland and Ontario have similar philosophies guiding early childhood education, with comparable child support systems and accessibility. The role of early childhood educators is also alike between Ontario and Scotland.
Business Process Outsourcing (BPO) involves contracting non-core business processes like back office functions to third-party providers, allowing companies to focus on core competencies. BPO offers economy of scale, superior outsourced skills through constant training, flexibility through scalable contracts, and lower costs than maintaining in-house operations. The global BPO market is worth over $1.6 billion and growing in popularity as outsourcing reduces risks associated with fluctuating staffing needs.
Centrecom provides a missed call solution using professionally trained agents to ensure customers always reach a live person. Their modular services are customized for each client's needs across industries like aviation, travel, tourism, and public services. For more details, contact Centrecom at info@centrecom.eu or +356 2364 4098.
Centrecom employs experienced communicators to conduct surveys for clients in fast moving consumer goods, insurance, and politics. Their agents are highly qualified to determine candidate popularity and collect relevant quantitative and qualitative data on national issues according to a client's specifications. Centrecom works closely with clients to design surveys, maintain schedules, and ensure projects meet expectations.
This teaching manual outlines a social science lesson plan about parliament constituencies for an 8th grade class of 26 students. The lesson aims to have students recall, discuss, interpret, organize, apply, and create innovations related to parliament constituencies. Instructional aids and activities are planned to be used, with an introduction, presentation of content, two activities, and a conclusion with review and enrichment. The document provides structure and objectives for the lesson.
A key missing piece is assessing the results of SEO efforts, by deriving weighted keywords and phrases from the publicly crawlable website content.
This "reverse engineering" of how search engines view a site is a classic example of web mining, and serves as a useful example to understand better the practical issues involved in real-world web mining.
Trevor Campbell - Creating a Global Infrastructure to Support China - SUGCONSUGCON
The document discusses strategies for scaling a Sitecore implementation to support China by addressing challenges posed by the Great Firewall of China (GFW). It recommends having infrastructure located in mainland China, using a .cn domain, and a Chinese DNS provider. For content distribution, it suggests replicating content from a North American instance to China via one-way SQL replication. For analytics, it outlines hosting the Experience Database in China or using a third-party Chinese analytics provider. It also provides guidance on localizing front-end integrations and caching assets for faster delivery in China.
The document discusses the open-source "tetrahedron" which is made up of four key elements - community, code, and infrastructure (testing and sharing). It provides guidance on growing and engaging a community, maintaining high-quality code, and leveraging continuous integration and artifact sharing to test code and disseminate the project.
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-opencv
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Gary Bradski, President and CEO of the OpenCV Foundation, presents the "OpenCV Open Source Computer Vision Library: Latest Developments" tutorial at the May 2015 Embedded Vision Summit.
OpenCV is an enormously popular open source computer vision library, with over 9 million downloads. Originally used mainly for research and prototyping, in recent years OpenCV has increasingly been used in deployed products on a wide range of platforms from cloud to mobile.
The latest version, OpenCV 3.0 is currently in beta, and is a major overhaul, bringing OpenCV up to modern C++ standards and incorporating expanded support for 3D vision. The new release also introduces a modular “contrib” facility that enables independently developed modules to be quickly integrated with OpenCV as needed, providing a flexible mechanism to allow developers to experiment with new techniques before they are officially integrated into the library.
In this talk, Gary Bradski, head of the OpenCV Foundation, provides an insider’s perspective on the new version of OpenCV and how developers can utilize it to maximum advantage for vision research, prototyping, and product development.
COM2304: Introduction to Computer Vision & Image Processing Hemantha Kulathilake
At the end of this lesson, you should be able to;
Describe image.
Describe digital image processing and computer vision.
Compare and Contrast image processing and computer vision.
Describe image sources.
Identify the array of imaging application under the EM Image source.
Describe the components of Image processing system and computer vision system.
OpenCV is an open-source library for computer vision and machine learning. The document discusses OpenCV's features including its modular structure, common computer vision algorithms like Canny edge detection, Hough transform, and cascade classifiers. Code examples are provided to demonstrate how to implement these algorithms using OpenCV functions and data types.
A search engine uses automated software programs called spiders that crawl the web to index pages and create a searchable database. When a user searches for keywords, the search engine software returns relevant results from the index. There are three main types of search engines - directories that are compiled by humans, hybrid engines that combine human and automated results, and meta search engines that search multiple other engines at once. Each search engine indexes pages differently and has a unique algorithm to determine search results.
The document discusses optimizing computer vision applications for cross-platform use. It describes conflicting requirements around being cross-platform versus utilizing specific device capabilities. Possible solutions discussed include optimizing for ARM NEON, a single platform, or all platforms. The document then introduces FastCV, a cross-platform computer vision library from Qualcomm that provides optimized implementations for different processors like Snapdragon to gain performance benefits while supporting multiple platforms.
Computer vision is a field that uses techniques to electronically perceive and understand images. It involves acquiring, processing, analyzing and understanding images and can take forms like video sequences. Computer vision aims to duplicate human vision abilities through artificial systems. It has applications in areas like manufacturing inspection, medical imaging, robotics, traffic monitoring and more. Some techniques used in computer vision include image acquisition, preprocessing, feature extraction, detection, recognition and interpretation.
This document discusses advances in image search and retrieval. It begins with an overview of visual information retrieval and its challenges, including the semantic gap between low-level visual features and high-level semantics. It then covers recent techniques like Google image search and similarity search. The document outlines core concepts like capturing similarity, large datasets, and user needs. It also revisits a 2000 paper on the challenges still facing the field, including the unsolved semantic gap and need for standardized evaluation benchmarks.
The document discusses different types of search engines. It describes search engines as programs that use keywords to search websites and return relevant results. It provides examples of popular search engines like Google, Yahoo, and Ask.com. It also explains different types of search engines such as crawler-based, directory-based, specialty, hybrid, and meta search engines. Finally, it discusses how to effectively use search engines through techniques like being specific, using symbols like + and -, and using Boolean searches.
The document discusses search engines, including how they work, their importance, and different types. It explains that search engines use crawlers to scan websites, extract keywords, and build databases. When users search, the engine returns relevant pages. Directories rely on human editors while hybrid engines use both crawlers and directories. Meta search engines transmit keywords to multiple engines and integrate results. Making effective searches involves keeping queries simple and considering how target pages may be described.
Scotland and Ontario have similar philosophies guiding early childhood education, with comparable child support systems and accessibility. The role of early childhood educators is also alike between Ontario and Scotland.
Business Process Outsourcing (BPO) involves contracting non-core business processes like back office functions to third-party providers, allowing companies to focus on core competencies. BPO offers economy of scale, superior outsourced skills through constant training, flexibility through scalable contracts, and lower costs than maintaining in-house operations. The global BPO market is worth over $1.6 billion and growing in popularity as outsourcing reduces risks associated with fluctuating staffing needs.
Centrecom provides a missed call solution using professionally trained agents to ensure customers always reach a live person. Their modular services are customized for each client's needs across industries like aviation, travel, tourism, and public services. For more details, contact Centrecom at info@centrecom.eu or +356 2364 4098.
Centrecom employs experienced communicators to conduct surveys for clients in fast moving consumer goods, insurance, and politics. Their agents are highly qualified to determine candidate popularity and collect relevant quantitative and qualitative data on national issues according to a client's specifications. Centrecom works closely with clients to design surveys, maintain schedules, and ensure projects meet expectations.
This teaching manual outlines a social science lesson plan about parliament constituencies for an 8th grade class of 26 students. The lesson aims to have students recall, discuss, interpret, organize, apply, and create innovations related to parliament constituencies. Instructional aids and activities are planned to be used, with an introduction, presentation of content, two activities, and a conclusion with review and enrichment. The document provides structure and objectives for the lesson.
A key missing piece is assessing the results of SEO efforts, by deriving weighted keywords and phrases from the publicly crawlable website content.
This "reverse engineering" of how search engines view a site is a classic example of web mining, and serves as a useful example to understand better the practical issues involved in real-world web mining.
Trevor Campbell - Creating a Global Infrastructure to Support China - SUGCONSUGCON
The document discusses strategies for scaling a Sitecore implementation to support China by addressing challenges posed by the Great Firewall of China (GFW). It recommends having infrastructure located in mainland China, using a .cn domain, and a Chinese DNS provider. For content distribution, it suggests replicating content from a North American instance to China via one-way SQL replication. For analytics, it outlines hosting the Experience Database in China or using a third-party Chinese analytics provider. It also provides guidance on localizing front-end integrations and caching assets for faster delivery in China.
- MOLAP refers to multidimensional OLAP, which implements OLAP using a multi-dimensional data structure known as a cube. Dimensions typically include factors like time, geography, and products.
- Cubes allow for fast retrieval of pre-aggregated data in near-constant time. Vendors provide proprietary query languages for analyzing cubes through pivots, drills, rolls, and slices.
- While MOLAP provides fast response times, it faces challenges of long load times to pre-calculate aggregates, sparse cubes wasting storage, and significant maintenance to aggregate new data. Partitioning and virtual cubes help address some of these issues.
SUGMEA - Sitecore Experience Platform - what's new in 9.3 previewdharmeshharji
The document summarizes the new features in Sitecore Experience Platform 9.3. Key updates include improved Sitecore forms with new elements like file upload and bot detection, an updated templating engine for SXA, scheduled plan enrollment for marketing automation, replacing the "Reach" metric with "Impressions" for experience optimization testing, and permission enabled search to filter search results based on user permissions. The installation process was also updated with new capabilities for Sitecore Install Assistant.
In this session discover how Oracle is running Oracle SOA Suite to support both modernization and innovation. Learn how SOA can run in Container as well as Kubernetes,
Core Web Vitals and Your Search Rankings Michael King
This document discusses Core Web Vitals and their importance to search engine rankings. It begins by introducing Core Web Vitals and their measurement metrics. It then explains how page speed has long been a ranking factor for Google, especially on mobile. The document dives into details on each Core Web Vital metric and how sites can optimize to improve scores. It also summarizes a study that found the vast majority of sites had poor Core Web Vitals scores prior to the Page Experience update rollout. The document stresses the importance of page speed and stability to users and search engines.
The document discusses Oracle NoSQL Database and its features. It provides an overview of NoSQL databases and data models in Oracle NoSQL including key-value, table, and JSON. It also describes Oracle NoSQL's architecture, which uses automatic data sharding and replication across storage nodes for high availability and scalability. Configuration and usage is simplified with libraries and command line tools.
The document discusses using classes from the .NET Framework base class library (BCL) to perform common tasks like working with files, strings, dates, generating random numbers, and getting system information. It covers the key classes for these tasks like File, Random, DateTime, and Environment. It also covers writing XML files using the XmlWriter class and controlling formatting with XmlWriterSettings. The overall purpose is to demonstrate how to utilize important .NET Framework classes to build application functionality.
Upgrading Made Easy: Moving to InfluxDB 2.x or InfluxDB Cloud with Cribl LogS...InfluxData
Many organizations agree that migrating workloads to the cloud or to a newer version of existing tooling can result in cost savings and flexibility. A well-designed observability pipeline is often the key to a quick and painless transition, leading to positive impacts on cost optimization, data visibility, and performance. Cribl’s LogStream product helps teams implement such an observability pipeline.
In this hands-on technical discussion, the audience will learn how to leverage Cribl LogStream to successfully upgrade from InfluxDB 1.x to InfluxDB 2.x or move to InfluxDB Cloud. Join us as we walk through the pros and cons of workload migration, share architecture best practices, and give a live demo on how to combine Cribl LogStream with the latest version of InfluxDB.
Set Your Content Free! : Case Studies from Netflix and NPRDaniel Jacobson
Last Friday (February 8th), I spoke at the Intelligent Content Conference 2013. When Scott Abel (aka The Content Wrangler) first contacted me to speak at the event, he asked me to speak about my content management and distribution experiences from both NPR and Netflix. The two experiences seemed to him to be an interesting blend for the conference. These are the slides from that presentation.
I have applied comments to every slide in this presentation to include the context that I otherwise provided verbally during the talk.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1uRYaAR.
Volker Pacher, Sam Phillips present key differences between relational databases and graph databases, and how they use the later to model a complex domain and to gain insights into their data. Filmed at qconlondon.com.
Sam Phillips is Head of Engineering for eBay's Local Delivery team, bringing super fast delivery to customers in the UK and US. Volker Pacher is a Senior Developer at eBay Local Delivery. Before its acquisition by eBay, he was a member of the core team at Shutl helping to transition from a monolithic application to SOA and introducing new technologies, among them Neo4j.
Sharded By Business Line: Migrating to a Core Database using MongoDB and SolrMongoDB
The Knot is a wedding planning website founded in 1996 that reaches 11 million users per month. It provides articles, tools, forums and other resources to help couples plan weddings. The director of software architecture, Jason Sirota, presented on the company's migration from a sharded SQL architecture to using multiple document databases like MongoDB in the cloud. This improved scalability and allowed integrating new data types more easily. MongoDB was chosen over other options as the primary document store. Challenges included dealing with differences in how UUIDs are stored between languages and initial driver errors. Load testing showed the new architecture could handle significantly more queries per minute than originally tested.
The Knot is a website focused on weddings, newlyweds and babies. It was founded in 1996 and now has 11 million unique visitors per month. It provides articles, photos, forums, planning tools and other resources for weddings. The presentation discusses migrating some of The Knot's systems to use open source and cloud technologies like MongoDB, Couchbase, AWS and Hadoop. It highlights some challenges faced like UUID endianness issues between C# and Python and phantom errors in the C# MongoDB driver. Traffic testing showed lower queries per minute than actual production traffic.
Content based image retrieval Projects.pdfrupaymts
Hello students!! Here I came up with new ideas about the Content Based Image Retrieval Project, Takeoff Edu group gives you an Innovative CBIR projects for final year students. Here we provide a CBIR and also all kinds of final year projects to you.
Content Based Image retrieval is not only enhances the efficiency of search engines but also opens up new avenues for image-based knowledge discovery and exploration. It has advanced algorithms and computer vision techniques to analyse and understand the visual content of images, allowing users to search for similar or related images based on visual similarities rather than textual descriptions.
Verndale - Sitecore User Group Los Angeles PresentationDavid Brown
The document discusses integrating Sitecore with third party systems using the Data Exchange Framework (DEF). DEF allows syncing of data between two systems and provides reusable integration capabilities. It extracts data from a source, transforms it, and loads it into a target system. Examples of using DEF to pull contacts from a CRM into Sitecore or update a contact in a CRM from Sitecore data are provided. The available DEF providers and how to code an integration to insert or update Salesforce records from Sitecore xDB data are also outlined. The document concludes with an overview of how to wire up a new DEF tenant to define the integration.
20191201 kubernetes managed weblogic revival - part 2makker_nl
This document discusses deploying WebLogic domains in Kubernetes using the WebLogic Kubernetes Operator. It provides an overview of the operator and how it can automate lifecycle operations for WebLogic domains running in Kubernetes. It also covers domain topologies, configuration overrides, assigning pods to nodes, and high availability and disaster recovery options for WebLogic on Kubernetes.
Six Different Things You Can Do In Kafka With Geo-ReplicationHostedbyConfluent
"Move it, share it, bridge it, stage it, backup it, optimize it, bop it. Did you know you can do these things with geo-replication in Kafka? (well, except bop it)
Kafka can stream data in real time between different clusters, regions, cloud environments (“geo-replication”) using tools like Apache Kafka® MirrorMaker 2 and Confluent Cluster Linking. Come hear six totally different things that companies around the world have used these tools for:
- Optimizing the latency and cost of their data streaming applications
- Sharing real-time data feeds with other departments, and even other companies
- Promoting workloads from staging environments to production
- Bridging the gap between the disconnected edge and the cloud
- Backing up their data for Disaster Recovery
- Moving from managing their own Kafka cluster to using a SaaS cluster"
Maintaining the Front Door to Netflix : The Netflix APIDaniel Jacobson
This presentation was given to the engineering organization at Zendesk. In this presentation, I talk about the challenges that the Netflix API faces in supporting the 1000+ different device types, millions of users, and billions of transactions. The topics range from resiliency, scale, API design, failure injection, continuous delivery, and more.
➒➌➎➏➑➐➋➑➐➐ Satta Matka Dpboss Matka Guessing Indian Matka
KALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA.COM | MATKA PANA JODI TODAY | BATTA SATKA | MATKA PATTI JODI NUMBER | MATKA RESULTS | MATKA CHART | MATKA JODI | SATTA COM | FULL RATE GAME | MATKA GAME | MATKA WAPKA | ALL MATKA RESULT LIVE ONLINE | MATKA RESULT | KALYAN MATKA RESULT | DPBOSS MATKA 143 | MAIN MATKA
➒➌➎➏➑➐➋➑➐➐ Matka Guessing Satta Matka Kalyan panel Chart Indian Matka Satta Matta Matka Dpboss KALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA.COM | MATKA PANA JODI TODAY | BATTA SATKA | MATKA PATTI JODI NUMBER | MATKA RESULTS | MATKA CHART | MATKA JODI | SATTA COM | FULL RATE GAME | MATKA GAME | MATKA WAPKA | ALL MATKA RESULT LIVE ONLINE | MATKA RESULT | KALYAN MATKA RESULT | DPBOSS MATKA 143 | MAIN MATKA
➒➌➎➏➑➐➋➑➐➐ Satta Matka Dpboss Matka GuessingKALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA.COM | MATKA PANA JODI TODAY | BATTA SATKA | MATKA PATTI JODI NUMBER | MATKA RESULTS | MATKA CHART | MATKA JODI | SATTA COM | FULL RATE GAME | MATKA GAME | MATKA WAPKA | ALL MATKA RESULT LIVE ONLINE | MATKA RESULT | KALYAN MATKA RESULT | DPBOSS MATKA 143 | MAIN MATKA
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➒➌➎➏➑➐➋➑➐➐KALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA.COM | MATKA PANA JODI TODAY | BATTA SATKA | MATKA PATTI JODI NUMBER | MATKA RESULTS | MATKA CHART | MATKA JODI | SATTA COM | FULL RATE GAME | MATKA GAME | MATKA WAPKA | ALL MATKA RESULT LIVE ONLINE | MATKA RESULT | KALYAN MATKA RESULT | DPBOSS MATKA 143 | MAIN MATKA
➒➌➎➏➑➐➋➑➐➐ Satta Matka Dpboss Matka Guessing Indian Matka
KALYAN MATKA | MATKA RESULT | KALYAN MATKA TIPS | SATTA MATKA | MATKA.COM | MATKA PANA JODI TODAY | BATTA SATKA | MATKA PATTI JODI NUMBER | MATKA RESULTS | MATKA CHART | MATKA JODI | SATTA COM | FULL RATE GAME | MATKA GAME | MATKA WAPKA | ALL MATKA RESULT LIVE ONLINE | MATKA RESULT | KALYAN MATKA RESULT | DPBOSS MATKA 143 | MAIN MATKA
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Image Search by KBK Group
1. An Image Indexing and Search
System for Large Databases
Stored on PCs and in Corporate
and Global Communication
Networks
KBK G ROUP LLC
N OVOSIBIRSK , R USSIA
D ECEMBER 2011
2. C OMPANY I NFORMATION
KBK Group LLC
Address: PO Box 114, Novosibirsk, 630001, Russia
Website (corporate): www.kbkgroup.org
Website (prototype): www.immenselab.com
Phone:
+ 7 913 891 0298 (English, French, German,
Italian, Serbian)
Email: akvalex@gmail.com
+7 913 915 0887 (Russian)
Email: info@kbkgroup.org
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
3. P ROBLEMS ADDRESSED BY
TECHNOLOGY
Problems of performance of search engines
when dealing with large databases of images.
High demands for computing power.
Users cannot control search results and have
to deal with millions of images.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
4. • Rights to use indexing kernel and search engine through an exclusive license.
• Know-how covers
•algorithms and a system of search and retrieval of images from Internet,
•Web-Crawlers,
•a Client – Server system to process user queries.
I NTELLECTUAL P ROPERTY R IGHTS
• Patent application was filed with the Russian Patent Office to cover
major features of retrieving the images from the databases.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
6. P ROTOTYPE OF I MAGE
S EARCH S ERVICE
WWW. IMMENSELAB . COM
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
7. H OW DOES IT WORK ? S TEP 1
A user uploads a
pattern to the Image
Search System
The Search System
returns identical
image if index
database contains it
or the most similar
if there is no
identical image in
the database.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
13. O N - THE - FLY SEARCH
ACCURACY CONTROL
By adjusting
search
accuracy (by
moving a
slider) a user
gets different
number of
search
results which
are similar to
the pattern.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
15. A UGMENTED SEARCH
Search pattern window Active result window
Even blurred
& damaged
images can
be used as a
pattern to
find similar
images.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
16. S EARCHING FOR SIMILAR
IMAGES
By
increasing
inaccuracy
of search we
can manage
retrieval of
similar
images.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
17. U SER I NTERFACE
User interface contains an array of sliders
and menus.
Sliders and menus allow a user to select
different options for a search and adjust the
range of accuracy/inaccuracy of search.
A user can change the settings and get
different query results in real time.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
18. T ESTS OF U SER I NTERFACE
Sample search results
19. S EARCH R ESULTS : B IRDS IN
SIMILAR LAKE
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
20. S NAPSHOTS OF U RBAN L ANDSCAPES : A
LOOK FROM DIFFERENT ANGLES
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
21. S EARCH FOR S IMILAR
L ANDSCAPES – WATERWAYS
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
22. S EARCH FOR A RCHITECTURAL
L ANDMARKS
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
23. S EARCH FOR THE SAME PERSON ( S )
DOING DIFFERENT THINGS
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
24. S EARCH FOR A C LUSTER OF
C ARS
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
25. S EARCH FOR THE MOON AND
CANALS
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
26. S EARCH FOR S IMILAR CARS
SHOT FROM DIFFERENT ANGLES
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
27. S EARCH FOR SIMILAR CARS
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
28. S EARCH BY THE SAME OR
RAGGED OUTLINE
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
29. S EARCH FOR THE SAME
FRAGMENTS IN DIFFERENT
PICTURES
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
30. S EARCH FOR THE SAME
BACKGROUND
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
31. S EARCH FOR L ANDSCAPES AT
NIGHT: DIFFERENT LIGHTS
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
33. C OMMERCIAL P OTENTIAL OF
THE T ECHNOLOGY
Internet users,
News agencies and publishers,
Advertising agencies,
Users / Social networks,
Licensees Mobile apps developers,
Patent offices and patent attorney firms,
Security companies,
Large museums and archives,
Movie distributors
Other owners of archives, photo and graphic databases.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
34. D EVELOPMENT STRATEGY
Development and promotion of proprietary
Three-tier Image Search Internet service as an ad and
strategy: commercial platform in Internet to
demonstrate the and attract potential users.
• Internet
Co-operation with major strategic partners to
• Partnerships
launch and distribute the Image Search
Software in international markets.
• Sales
Selling software licenses independently and
through existing distribution channels.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
35. C OMPETITORS
www.Tineye.com & www.gazopa.com – search of
images similar to a pattern.
www.wesee.com, www.imprezzeo.com – claim
Image they’ve got image search systems by content,
Search however prototypes have not been demonstrated.
Services Google Goggles and Google Chrome search by a
pattern.
http://visual.images.yandex.ru/sights/ - Yandex
visual search.
www.picsearch.com – image search by tags.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
36. C OMPETITIVE A DVANTAGES
Compact indexing: Innovation indexes used by the system
are 100 times more compact that B-Tree indexes and
Particular provide 100 times higher retrieval rate than B-Tree
problems indexes.
which the Indexed databases can contain hundreds of billions of
images.
technology
solves A user controls the search, i.e. he/she selects criteria
before the search (colour, outline, tag, etc.).
High performance ranked (i.e. building a line of results
depending on similarity rate) image search by a pattern
with accuracy/inaccuracy controlled by a user in real time.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
37. O THER APPLICATIONS
Development of Green IT projects aimed at
reduction of carbon footprint due to saving
index storage space by order of 100.
Using
Using technology with Cloud Computing will
technology
increase performance of search engines
advantages dramatically.
Mobile applications to develop mobile image
search technologies. It cuts down traffic load
considerably by working with small index
databases.
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG
38. T HANK YOU ! A ND
H AVE A GOOD SEARCH !
(C) KBK GROUP LLC - 2011 WWW.KBKGROUP.ORG