This document summarizes a research paper that proposes a method for fine-grained dog breed classification using part localization. The method detects faces, localizes key parts like eyes and nose, and extracts features from the localized parts to classify breeds. It finds part locations by consensus of similar exemplar dogs weighted by detector responses. The pipeline detects faces, localizes parts, infers ears, and classifies breeds using features from localized parts. The method is evaluated on a dataset of over 8,000 dog images from 133 breeds and achieves accurate classification on images with diverse poses and appearances.
This presentation displays the applications of CNNs, a quick review about Neural Networks and their drawbacks, the convolution process, padding, striding, convolution over volume, types of layers in CNN, max pool layer, fully connected layer, and lastly the famous CNNs, LetNet-5, AlexNet, VGG-16, ResNet and GoogLeNet.
Depression Detection Based On Title Of VideoIJSRED
This paper presents a method to detect depression based on analyzing the titles of videos watched by users on social media platforms like YouTube. The system would gather video title and viewing data from a user's YouTube account. Using natural language processing and machine learning techniques, the system would analyze the emotions and topics contained in the titles of watched videos to detect if the user is exhibiting signs of depression. If detected, the system would provide social support to the user by automatically contacting close friends and family members. The goal is to help identify depressed users early and provide support before their condition worsens.
1. The document discusses challenges with standard reinforcement learning formulations due to large state and action spaces. It proposes representing actions as operators that induce state transitions rather than discrete choices.
2. It introduces a generalized reinforcement learning framework using kernel methods to compare "decision contexts" or state-action pairs. Value functions are represented as vectors in a Reproducing Kernel Hilbert Space rather than concrete mappings.
3. Gaussian process regression is used to predict values for unseen state-action pairs by comparing them to stored samples, enabling generalization beyond explored contexts. Hyperparameters are tuned to best explain sample data using marginal likelihood optimization.
This document describes the digital differential analyzer (DDA) algorithm for rasterizing lines, triangles, and polygons in computer graphics. It discusses implementing DDA using floating-point or integer arithmetic. The DDA line drawing algorithm works by incrementing either the x or y coordinate by 1 each step depending on whether the slope is less than or greater than 1. Pseudocode is provided to illustrate the algorithm. Potential drawbacks of DDA are also mentioned, such as the expense of rounding operations.
The document provides guidance on building an end-to-end machine learning project to predict California housing prices using census data. It discusses getting real data from open data repositories, framing the problem as a supervised regression task, preparing the data through cleaning, feature engineering, and scaling, selecting and training models, and evaluating on a held-out test set. The project emphasizes best practices like setting aside test data, exploring the data for insights, using pipelines for preprocessing, and techniques like grid search, randomized search, and ensembles to fine-tune models.
Data center virtualization (DCV) involves converting hardware resources like servers, storage and networking equipment in a data center into virtual resources that can be easily managed and allocated. This allows several virtual machines to run on a single physical server, reducing costs associated with power, cooling and hardware. DCV provides benefits like energy savings, easier backups, reduced costs and vendor independence by using a hypervisor to manage virtual machines independently of underlying hardware. However, issues with DCV include increased security risks, potential performance issues with certain applications, and increased licensing costs.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
This presentation displays the applications of CNNs, a quick review about Neural Networks and their drawbacks, the convolution process, padding, striding, convolution over volume, types of layers in CNN, max pool layer, fully connected layer, and lastly the famous CNNs, LetNet-5, AlexNet, VGG-16, ResNet and GoogLeNet.
Depression Detection Based On Title Of VideoIJSRED
This paper presents a method to detect depression based on analyzing the titles of videos watched by users on social media platforms like YouTube. The system would gather video title and viewing data from a user's YouTube account. Using natural language processing and machine learning techniques, the system would analyze the emotions and topics contained in the titles of watched videos to detect if the user is exhibiting signs of depression. If detected, the system would provide social support to the user by automatically contacting close friends and family members. The goal is to help identify depressed users early and provide support before their condition worsens.
1. The document discusses challenges with standard reinforcement learning formulations due to large state and action spaces. It proposes representing actions as operators that induce state transitions rather than discrete choices.
2. It introduces a generalized reinforcement learning framework using kernel methods to compare "decision contexts" or state-action pairs. Value functions are represented as vectors in a Reproducing Kernel Hilbert Space rather than concrete mappings.
3. Gaussian process regression is used to predict values for unseen state-action pairs by comparing them to stored samples, enabling generalization beyond explored contexts. Hyperparameters are tuned to best explain sample data using marginal likelihood optimization.
This document describes the digital differential analyzer (DDA) algorithm for rasterizing lines, triangles, and polygons in computer graphics. It discusses implementing DDA using floating-point or integer arithmetic. The DDA line drawing algorithm works by incrementing either the x or y coordinate by 1 each step depending on whether the slope is less than or greater than 1. Pseudocode is provided to illustrate the algorithm. Potential drawbacks of DDA are also mentioned, such as the expense of rounding operations.
The document provides guidance on building an end-to-end machine learning project to predict California housing prices using census data. It discusses getting real data from open data repositories, framing the problem as a supervised regression task, preparing the data through cleaning, feature engineering, and scaling, selecting and training models, and evaluating on a held-out test set. The project emphasizes best practices like setting aside test data, exploring the data for insights, using pipelines for preprocessing, and techniques like grid search, randomized search, and ensembles to fine-tune models.
Data center virtualization (DCV) involves converting hardware resources like servers, storage and networking equipment in a data center into virtual resources that can be easily managed and allocated. This allows several virtual machines to run on a single physical server, reducing costs associated with power, cooling and hardware. DCV provides benefits like energy savings, easier backups, reduced costs and vendor independence by using a hypervisor to manage virtual machines independently of underlying hardware. However, issues with DCV include increased security risks, potential performance issues with certain applications, and increased licensing costs.
Object Detection using Deep Neural NetworksUsman Qayyum
Recent Talk at PI school covering following contents
Object Detection
Recent Architecture of Deep NN for Object Detection
Object Detection on Embedded Computers (or for edge computing)
SqueezeNet for embedded computing
TinySSD (object detection for edge computing)
This document provides information on setting up wireless simulations in NS-2 including:
1) Details on configuring wireless node parameters, channels, propagation models, interfaces, and routing protocols.
2) Examples of generating node mobility using the setdest script and generating traffic using cbrgen.
3) The format of DSR trace files and how to calculate routing overhead and packet delivery ratio from these files using AWK.
The document discusses authentication issues in cloud computing. It notes that as more companies migrate services and data to the cloud, secure authentication is a major concern. Key issues include single points of failure, data breaches due to weak authentication methods, lack of control over authentication in public clouds, and managing multiple user accounts and authentication processes across different cloud services. The document examines authentication challenges associated with various cloud deployment models and the difficulty of synchronizing authentication between internal and external cloud systems.
Xen is a virtual machine monitor that allows multiple guest operating systems to run simultaneously on the same computer hardware. It uses paravirtualization, where the guest operating systems are modified to interface with the hypervisor rather than directly with hardware. This allows Xen to provide isolation between guest virtual machines while maintaining high performance. Xen introduces a new privileged level, where the hypervisor runs at a higher privilege than the guest operating systems. This allows Xen to maintain control over CPU, memory, and I/O access between virtual machines.
The search for faster computing remains of great importance to the software community. Relatively inexpensive modern hardware, such as GPUs, allows users to run highly parallel code on thousands, or even millions of cores on distributed systems.
Building efficient GPU software is not a trivial task, often requiring a significant amount of engineering hours to attain the best performance. Similarly, distributed computing systems are inherently complex. In recent years, several libraries were developed to solve such problems. However, they often target a single aspect of computing, such as GPU computing with libraries like CuPy, or distributed computing with Dask.
Libraries like Dask and CuPy tend to provide great performance while abstracting away the complexity from non-experts, being great candidates for developers writing software for various different applications. Unfortunately, they are often difficult to be combined, at least efficiently.
With the recent introduction of NumPy community standards and protocols, it has become much easier to integrate any libraries that share the already well-known NumPy API. Such changes allow libraries like Dask, known for its easy-to-use parallelization and distributed computing capabilities, to defer some of that work to other libraries such as CuPy, providing users the benefits from both distributed and GPU computing with little to no change in their existing software built using the NumPy API.
The document discusses different techniques for filling polygons, including boundary fill, flood fill, and scan-line fill methods. It provides details on how each technique works, such as using a seed point and filling neighboring pixels for boundary fill, replacing all pixels of a selected color for flood fill, and drawing pixels between edge intersections for each scan line for scan-line fill. Examples are given to illustrate the filling process for each method.
Text Extraction from Product Images Using State-of-the-Art Deep Learning Tech...Databricks
Extracting texts of various sizes, shapes and orientations from images containing multiple objects is an important problem in many contexts, especially, in connection to e-commerce, augmented reality assistance system in a natural scene, content moderation in social media platform, etc. The text from the image can be a richer and more accurate source of data than human inputs which can be used in several applications like Attribute Extraction, Offensive Text Classification, Product Matching, Compliance use cases, etc.
YouTube: https://youtu.be/LzaWrmKL1Z4
** Python Data Science Training: https://www.edureka.co/python **
In this PPT on “Reinforcement Learning Tutorial” you will get an in-depth understanding about how reinforcement learning is used in the real world. I’ll be covering the following topics in this session:
Introduction to Machine Learning
What is Reinforcement Learning?
Reinforcement Learning with an analogy
Reinforcement Learning process
Reinforcement Learning Counter-Strike example
Reinforcement Learning Definitions
Reinforcement Learning Concepts
Markov’s Decision Process
Understanding Q-Learning
Demo
Check out our Python Training Playlist: https://goo.gl/Na1p9G
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document summarizes the K-means clustering algorithm. It provides an outline of the topics covered, which include an introduction to clustering and K-means, how to calculate K-means using steps 0 through 2, results and suggestions, and references. It then provides more detail on the three steps of K-means: 1) initialize centroids, 2) assign points to closest centroids, and 3) recalculate centroids. Pseudocode is provided to demonstrate how to code K-means in Visual Basic.
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
Bayesian classification is a statistical classification method that uses Bayes' theorem to calculate the probability of class membership. It provides probabilistic predictions by calculating the probabilities of classes for new data based on training data. The naive Bayesian classifier is a simple Bayesian model that assumes conditional independence between attributes, allowing faster computation. Bayesian belief networks are graphical models that represent dependencies between variables using a directed acyclic graph and conditional probability tables.
This document provides an overview of Vehicular Ad-Hoc Networks (VANETs). It discusses the key components of VANETs including on-board units, roadside units, and a trusted authority. It describes the different types of communication in VANETs and lists some of the main applications like safety and convenience applications. The document also outlines some of the security requirements for VANETs, challenges in deploying them at scale, and techniques for establishing trust between vehicles.
The document provides an overview of three transport layer protocols: UDP, TCP, and SCTP. It discusses their features such as connection-oriented vs connectionless delivery, reliable vs unreliable transmission, and use of ports, segments, and packets for process-to-process communication. The document also includes figures illustrating concepts like multiplexing, sliding windows, and error control mechanisms in TCP and SCTP.
The document discusses cloud computing security. It begins with an introduction to cloud computing that defines it and outlines its characteristics, service models, and deployment models. It then discusses common security concerns and attacks in cloud computing like DDoS attacks, side channel attacks, and attacks on management consoles. It provides best practices for different security domains like architecture, governance, compliance, and data security. It also discusses current industry initiatives in cloud security.
Sample Network Analysis Report based on Wireshark AnalysisDavid Sweigert
This network analysis report examines a packet capture file containing traffic between two internal hosts downloading a file from a remote server. The analysis found that one internal host, with IP ending in 1.119, experienced significant packet loss during the download, as shown by drops in throughput and bursts of TCP errors. This packet loss indicates a potential failure at an infrastructure device, likely causing the observed retransmissions and degradation in performance. Further analysis of ingress traffic is needed to determine if the packet loss is occurring internally or externally to the network.
Image classification using convolutional neural networkKIRAN R
For separating the images from a large collection of images or from a large dataset this classifier can be used, Here deep neural network is used for training and classifying the images. The convolutional neural network is the most suitable algorithm for classifier images. This Classifier is a machine learning model, so the more you train it the more will be the accuracy.
Disease prediction using machine learningJinishaKG
Github link :
https://github.com/jini-the-coder/Diseaseprediction
Blog link :
http://amigoscreation.blogspot.com/2020/07/disease-prediction-using-machine.html
Youtube link :
https://youtu.be/3YmAbta16yk
This document discusses object detection using the Single Shot Detector (SSD) algorithm with the MobileNet V1 architecture. It begins with an introduction to object detection and a literature review of common techniques. It then describes the basic architecture of convolutional neural networks and how they are used for feature extraction in SSD. The SSD framework uses multi-scale feature maps for detection and convolutional predictors. MobileNet V1 reduces model size and complexity through depthwise separable convolutions. This allows SSD with MobileNet V1 to perform real-time object detection with reduced parameters and computations compared to other models.
DQDB is a distributed queue dual bus protocol for metropolitan area networks. It uses two unidirectional logical busses and the queued-packet distributed switch algorithm to transmit both data and multimedia traffic. DQDB operates at the data link layer and provides connection-oriented, connectionless, and asynchronous services. Stations can transmit to downstream nodes on one bus and upstream nodes on the other bus.
Linux originated from Linus Benedict, who created the first version as a free and open-source alternative to Unix. Linux uses a kernel that connects software applications to hardware. It has become popular in mobile devices due to its ability to support a wide variety of hardware and formats. Some advantages of Linux in mobile devices are that it is open-source, secure, and has strong hardware support. However, it can be less user-friendly and require more support than other mobile operating systems. Overall, Linux is recommended for mobile devices when security is a top priority.
(2017/06)Practical points of deep learning for medical imagingKyuhwan Jung
This document provides an overview of deep learning and its applications in medical imaging. It discusses key topics such as the definition of artificial intelligence, a brief history of neural networks and machine learning, and how deep learning is driving breakthroughs in tasks like visual and speech recognition. The document also addresses challenges in medical data analysis using deep learning, such as how to handle limited data or annotations. It provides examples of techniques used to address these challenges, such as data augmentation, transfer learning, and weakly supervised learning.
Supervised learning is a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns. Unlike unsupervised learning, supervised learning algorithms are given labeled training to learn the relationship between the input and the outputs.
Supervised machine learning algorithms make it easier for organizations to create complex models that can make accurate predictions. As a result, they are widely used across various industries and fields, including healthcare, marketing, financial services, and more.
Here, we’ll cover the fundamentals of supervised learning in AI, how supervised learning algorithms work, and some of its most common use cases.
Get started for free
How does supervised learning work?
The data used in supervised learning is labeled — meaning that it contains examples of both inputs (called features) and correct outputs (labels). The algorithms analyze a large dataset of these training pairs to infer what a desired output value would be when asked to make a prediction on new data.
For instance, let’s pretend you want to teach a model to identify pictures of trees. You provide a labeled dataset that contains many different examples of types of trees and the names of each species. You let the algorithm try to define what set of characteristics belongs to each tree based on the labeled outputs. You can then test the model by showing it a tree picture and asking it to guess what species it is. If the model provides an incorrect answer, you can continue training it and adjusting its parameters with more examples to improve its accuracy and minimize errors.
Once the model has been trained and tested, you can use it to make predictions on unknown data based on the previous knowledge it has learned.
How does supervised learning work?
The data used in supervised learning is labeled — meaning that it contains examples of both inputs (called features) and correct outputs (labels). The algorithms analyze a large dataset of these training pairs to infer what a desired output value would be when asked to make a prediction on new data.
For instance, let’s pretend you want to teach a model to identify pictures of trees. You provide a labeled dataset that contains many different examples of types of trees and the names of each species. You let the algorithm try to define what set of characteristics belongs to each tree based on the labeled outputs. You can then test the model by showing it a tree picture and asking it to guess what species it is. If the model provides an incorrect answer, you can continue training it and adjusting its parameters with more examples to improve its accuracy and minimize errors.
Once the model has been trained and tested, you can use it to make predictions on unknown data based on the previous knowledge it has learned.
Types of supervised learning
Supervised learning in machine learning is generally divided into two categories: classification and regre
The document discusses instance-based learning methods. It introduces k-nearest neighbors classification and locally weighted regression. For k-nearest neighbors, it explains how to determine the number of neighbors k through validation and describes how to handle both discrete and real-valued classification problems. Locally weighted regression predicts values based on a weighted average of nearby points, where the weights depend on each point's distance from the query instance.
This document provides information on setting up wireless simulations in NS-2 including:
1) Details on configuring wireless node parameters, channels, propagation models, interfaces, and routing protocols.
2) Examples of generating node mobility using the setdest script and generating traffic using cbrgen.
3) The format of DSR trace files and how to calculate routing overhead and packet delivery ratio from these files using AWK.
The document discusses authentication issues in cloud computing. It notes that as more companies migrate services and data to the cloud, secure authentication is a major concern. Key issues include single points of failure, data breaches due to weak authentication methods, lack of control over authentication in public clouds, and managing multiple user accounts and authentication processes across different cloud services. The document examines authentication challenges associated with various cloud deployment models and the difficulty of synchronizing authentication between internal and external cloud systems.
Xen is a virtual machine monitor that allows multiple guest operating systems to run simultaneously on the same computer hardware. It uses paravirtualization, where the guest operating systems are modified to interface with the hypervisor rather than directly with hardware. This allows Xen to provide isolation between guest virtual machines while maintaining high performance. Xen introduces a new privileged level, where the hypervisor runs at a higher privilege than the guest operating systems. This allows Xen to maintain control over CPU, memory, and I/O access between virtual machines.
The search for faster computing remains of great importance to the software community. Relatively inexpensive modern hardware, such as GPUs, allows users to run highly parallel code on thousands, or even millions of cores on distributed systems.
Building efficient GPU software is not a trivial task, often requiring a significant amount of engineering hours to attain the best performance. Similarly, distributed computing systems are inherently complex. In recent years, several libraries were developed to solve such problems. However, they often target a single aspect of computing, such as GPU computing with libraries like CuPy, or distributed computing with Dask.
Libraries like Dask and CuPy tend to provide great performance while abstracting away the complexity from non-experts, being great candidates for developers writing software for various different applications. Unfortunately, they are often difficult to be combined, at least efficiently.
With the recent introduction of NumPy community standards and protocols, it has become much easier to integrate any libraries that share the already well-known NumPy API. Such changes allow libraries like Dask, known for its easy-to-use parallelization and distributed computing capabilities, to defer some of that work to other libraries such as CuPy, providing users the benefits from both distributed and GPU computing with little to no change in their existing software built using the NumPy API.
The document discusses different techniques for filling polygons, including boundary fill, flood fill, and scan-line fill methods. It provides details on how each technique works, such as using a seed point and filling neighboring pixels for boundary fill, replacing all pixels of a selected color for flood fill, and drawing pixels between edge intersections for each scan line for scan-line fill. Examples are given to illustrate the filling process for each method.
Text Extraction from Product Images Using State-of-the-Art Deep Learning Tech...Databricks
Extracting texts of various sizes, shapes and orientations from images containing multiple objects is an important problem in many contexts, especially, in connection to e-commerce, augmented reality assistance system in a natural scene, content moderation in social media platform, etc. The text from the image can be a richer and more accurate source of data than human inputs which can be used in several applications like Attribute Extraction, Offensive Text Classification, Product Matching, Compliance use cases, etc.
YouTube: https://youtu.be/LzaWrmKL1Z4
** Python Data Science Training: https://www.edureka.co/python **
In this PPT on “Reinforcement Learning Tutorial” you will get an in-depth understanding about how reinforcement learning is used in the real world. I’ll be covering the following topics in this session:
Introduction to Machine Learning
What is Reinforcement Learning?
Reinforcement Learning with an analogy
Reinforcement Learning process
Reinforcement Learning Counter-Strike example
Reinforcement Learning Definitions
Reinforcement Learning Concepts
Markov’s Decision Process
Understanding Q-Learning
Demo
Check out our Python Training Playlist: https://goo.gl/Na1p9G
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
This document summarizes the K-means clustering algorithm. It provides an outline of the topics covered, which include an introduction to clustering and K-means, how to calculate K-means using steps 0 through 2, results and suggestions, and references. It then provides more detail on the three steps of K-means: 1) initialize centroids, 2) assign points to closest centroids, and 3) recalculate centroids. Pseudocode is provided to demonstrate how to code K-means in Visual Basic.
classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.
Bayesian classification is a statistical classification method that uses Bayes' theorem to calculate the probability of class membership. It provides probabilistic predictions by calculating the probabilities of classes for new data based on training data. The naive Bayesian classifier is a simple Bayesian model that assumes conditional independence between attributes, allowing faster computation. Bayesian belief networks are graphical models that represent dependencies between variables using a directed acyclic graph and conditional probability tables.
This document provides an overview of Vehicular Ad-Hoc Networks (VANETs). It discusses the key components of VANETs including on-board units, roadside units, and a trusted authority. It describes the different types of communication in VANETs and lists some of the main applications like safety and convenience applications. The document also outlines some of the security requirements for VANETs, challenges in deploying them at scale, and techniques for establishing trust between vehicles.
The document provides an overview of three transport layer protocols: UDP, TCP, and SCTP. It discusses their features such as connection-oriented vs connectionless delivery, reliable vs unreliable transmission, and use of ports, segments, and packets for process-to-process communication. The document also includes figures illustrating concepts like multiplexing, sliding windows, and error control mechanisms in TCP and SCTP.
The document discusses cloud computing security. It begins with an introduction to cloud computing that defines it and outlines its characteristics, service models, and deployment models. It then discusses common security concerns and attacks in cloud computing like DDoS attacks, side channel attacks, and attacks on management consoles. It provides best practices for different security domains like architecture, governance, compliance, and data security. It also discusses current industry initiatives in cloud security.
Sample Network Analysis Report based on Wireshark AnalysisDavid Sweigert
This network analysis report examines a packet capture file containing traffic between two internal hosts downloading a file from a remote server. The analysis found that one internal host, with IP ending in 1.119, experienced significant packet loss during the download, as shown by drops in throughput and bursts of TCP errors. This packet loss indicates a potential failure at an infrastructure device, likely causing the observed retransmissions and degradation in performance. Further analysis of ingress traffic is needed to determine if the packet loss is occurring internally or externally to the network.
Image classification using convolutional neural networkKIRAN R
For separating the images from a large collection of images or from a large dataset this classifier can be used, Here deep neural network is used for training and classifying the images. The convolutional neural network is the most suitable algorithm for classifier images. This Classifier is a machine learning model, so the more you train it the more will be the accuracy.
Disease prediction using machine learningJinishaKG
Github link :
https://github.com/jini-the-coder/Diseaseprediction
Blog link :
http://amigoscreation.blogspot.com/2020/07/disease-prediction-using-machine.html
Youtube link :
https://youtu.be/3YmAbta16yk
This document discusses object detection using the Single Shot Detector (SSD) algorithm with the MobileNet V1 architecture. It begins with an introduction to object detection and a literature review of common techniques. It then describes the basic architecture of convolutional neural networks and how they are used for feature extraction in SSD. The SSD framework uses multi-scale feature maps for detection and convolutional predictors. MobileNet V1 reduces model size and complexity through depthwise separable convolutions. This allows SSD with MobileNet V1 to perform real-time object detection with reduced parameters and computations compared to other models.
DQDB is a distributed queue dual bus protocol for metropolitan area networks. It uses two unidirectional logical busses and the queued-packet distributed switch algorithm to transmit both data and multimedia traffic. DQDB operates at the data link layer and provides connection-oriented, connectionless, and asynchronous services. Stations can transmit to downstream nodes on one bus and upstream nodes on the other bus.
Linux originated from Linus Benedict, who created the first version as a free and open-source alternative to Unix. Linux uses a kernel that connects software applications to hardware. It has become popular in mobile devices due to its ability to support a wide variety of hardware and formats. Some advantages of Linux in mobile devices are that it is open-source, secure, and has strong hardware support. However, it can be less user-friendly and require more support than other mobile operating systems. Overall, Linux is recommended for mobile devices when security is a top priority.
(2017/06)Practical points of deep learning for medical imagingKyuhwan Jung
This document provides an overview of deep learning and its applications in medical imaging. It discusses key topics such as the definition of artificial intelligence, a brief history of neural networks and machine learning, and how deep learning is driving breakthroughs in tasks like visual and speech recognition. The document also addresses challenges in medical data analysis using deep learning, such as how to handle limited data or annotations. It provides examples of techniques used to address these challenges, such as data augmentation, transfer learning, and weakly supervised learning.
Supervised learning is a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns. Unlike unsupervised learning, supervised learning algorithms are given labeled training to learn the relationship between the input and the outputs.
Supervised machine learning algorithms make it easier for organizations to create complex models that can make accurate predictions. As a result, they are widely used across various industries and fields, including healthcare, marketing, financial services, and more.
Here, we’ll cover the fundamentals of supervised learning in AI, how supervised learning algorithms work, and some of its most common use cases.
Get started for free
How does supervised learning work?
The data used in supervised learning is labeled — meaning that it contains examples of both inputs (called features) and correct outputs (labels). The algorithms analyze a large dataset of these training pairs to infer what a desired output value would be when asked to make a prediction on new data.
For instance, let’s pretend you want to teach a model to identify pictures of trees. You provide a labeled dataset that contains many different examples of types of trees and the names of each species. You let the algorithm try to define what set of characteristics belongs to each tree based on the labeled outputs. You can then test the model by showing it a tree picture and asking it to guess what species it is. If the model provides an incorrect answer, you can continue training it and adjusting its parameters with more examples to improve its accuracy and minimize errors.
Once the model has been trained and tested, you can use it to make predictions on unknown data based on the previous knowledge it has learned.
How does supervised learning work?
The data used in supervised learning is labeled — meaning that it contains examples of both inputs (called features) and correct outputs (labels). The algorithms analyze a large dataset of these training pairs to infer what a desired output value would be when asked to make a prediction on new data.
For instance, let’s pretend you want to teach a model to identify pictures of trees. You provide a labeled dataset that contains many different examples of types of trees and the names of each species. You let the algorithm try to define what set of characteristics belongs to each tree based on the labeled outputs. You can then test the model by showing it a tree picture and asking it to guess what species it is. If the model provides an incorrect answer, you can continue training it and adjusting its parameters with more examples to improve its accuracy and minimize errors.
Once the model has been trained and tested, you can use it to make predictions on unknown data based on the previous knowledge it has learned.
Types of supervised learning
Supervised learning in machine learning is generally divided into two categories: classification and regre
The document discusses instance-based learning methods. It introduces k-nearest neighbors classification and locally weighted regression. For k-nearest neighbors, it explains how to determine the number of neighbors k through validation and describes how to handle both discrete and real-valued classification problems. Locally weighted regression predicts values based on a weighted average of nearby points, where the weights depend on each point's distance from the query instance.
Ensemble classification techniques for detecting signatures of natural select...Andrew Stewart
This document introduces SFselect-E, an ensemble classification technique for detecting signatures of natural selection from site frequency spectra (SFS). SFselect-E builds upon existing methods SFselect and Multi-K by taking either a bagging or Multi-K clustering approach. Experimental results on population simulations show that while SFselect-E performs comparably to SFselect, limitations in how the training data was structured prevented the ensemble from generating specialized classifiers. Future work to refine the training data and preprocessing is needed to fully realize the potential of the ensemble approach.
This document summarizes a presentation on using string kernels for text classification. It introduces text classification and the challenge of representing text documents as feature vectors. It then discusses how kernel methods can be used as an alternative, by mapping documents into a feature space without explicitly extracting features. Different string kernel algorithms are described that measure similarity between documents based on common subsequences of characters. The document evaluates the performance of these kernels on a text dataset and explores ways to improve efficiency, such as through kernel approximation.
Local Outlier Detection with InterpretationDaiki Tanaka
This paper proposes a method called Local Outlier Detection with Interpretation (LODI) that detects outliers and explains their anomalousness simultaneously. LODI first selects a neighboring set for each outlier candidate using entropy measures. It then computes an anomaly degree for each object based on its deviation from neighbors in a learned 1D subspace. Finally, LODI interprets outliers by identifying a small set of influential features. Experiments on synthetic and real-world data show LODI outperforms other methods in outlier detection and provides intuitive feature-based explanations. However, LODI's computation is expensive and it assumes linear separability, which are limitations for future work.
This document provides an introduction to clustering, an unsupervised learning technique. Clustering involves grouping unlabeled data points into clusters such that objects within a cluster are similar to each other and dissimilar to objects in other clusters. The goal of clustering is to maximize similarity within clusters and minimize similarity between clusters. Several clustering algorithms are described, including hierarchical clustering which creates nested clusters, and partitional clustering which divides data into a set number of partitions. Key steps in clustering include selecting features, collecting data, choosing an algorithm, specifying the number of clusters, and evaluating the results.
3D Scene Analysis via Sequenced Predictions over Points and RegionsFlavia Grosan
I gave this talk in Machine Vision seminar at Jacobs University. I presented the state of the art in 3D point cloud classification and I described X. Xiong et al approach in a paper published in 2010.
- X-ray interferometry could enable unprecedented resolution, improving on the Hubble Space Telescope by orders of magnitude. Laboratory tests have demonstrated interferometry is possible using simple configurations of grazing incidence flats/mirrors. A space-based observatory design is proposed using an array of flats separated by distances up to kilometers to form fringes and image celestial sources. The technique could have applications for high-contrast imaging of exoplanets by taking advantage of reduced scattering at grazing incidence angles.
In this talk I will discuss how to deduplicate large amounts of source code using the source{d} stack, and more specifically the Apollo project. The 3 steps of the process used in Apollo will be detailed, ie: - the feature extraction step; - the hashing step; - the connected component and community detection step; I'll then go on describing some of the results found from applying Apollo to Public Git Archive, as well as the issues I faced and how these issues could have been somewhat avoided. The talk will be concluded by discussing Gemini, the production-ready sibling project to Apollo, and imagining applications that could extract value from Apollo.
After a quick introduction on the motivation behind Apollo, as said in the abstract I'll describe each step of Apollo's process. As a rule of thumb I'll first describe it formally, then go into how we did it in practice.
Feature extraction: I'll describe code representation, specifically as UASTs, then from there detail the features used. This will allow me to differentiate Apollo from it's inspiration, DejaVu, and talk about code clones taxonomy a bit. TF-IDF will also be touched upon. Hashing: I'll describe the basic Minhashing algorithm, then the improvements Sergey Ioffe's variant brought. I'll justify it's use in our case simultaneously. Connected components/Community detection: I'll describe the connected components and community notion's first (as in in graphs), then talk about the different ways we can extract them from the similarity graph.
After this I'll talk about the issues I had applying Apollo to PGA due to the amount of data, and how I went around the major issued faced. Then I'll go on talking about the results, show some of the communities, and explain in light of these results how issues could have been avoided, and the whole process improved. Finally I'll talk about Gemini, and outline some of the applications that could be imagined to Source code Deduplication.
This document provides an overview of Dirichlet processes and their applications. It begins with background on probability mass functions and density functions. It then discusses the probability simplex and the Dirichlet distribution. The Dirichlet process is defined as a distribution over distributions that allows modeling probability distributions over infinite sample spaces. An example application involves using Dirichlet processes to learn hierarchical morphology paradigms by modeling stems and suffixes as being generated independently from Dirichlet processes. References for further reading are also provided.
System 1 and System 2 were basic early systems for image matching that used color and texture matching. Descriptor-based approaches like SIFT provided more invariance but not perfect invariance. Patch descriptors like SIFT were improved by making them more invariant to lighting changes like color and illumination shifts. The best performance came from combining descriptors with color invariance. Representing images as histograms of visual word occurrences captured patterns in local image patches and allowed measuring similarity between images. Large vocabularies of visual words provided more discriminative power but were costly to compute and store.
This document discusses the K-nearest neighbors (KNN) algorithm, an instance-based learning method used for classification. KNN works by identifying the K training examples nearest to a new data point and assigning the most common class among those K neighbors to the new point. The document covers how KNN calculates distances between data points, chooses the value of K, handles feature normalization, and compares strengths and weaknesses of the approach. It also briefly discusses clustering, an unsupervised learning technique where data is grouped based on similarity.
R exam (B) given in Paris-Dauphine, Licence Mido, Jan. 11, 2013Christian Robert
This is one of two exams given to our students this year. They had two hours to solve three problems and had to return R codes as well as handwritten explanations.
- Fourier shell correlation (FSC) is used to estimate resolution in cryo-EM by measuring the correlation between two independent half maps in Fourier space shells.
- True resolution varies locally within cryo-EM maps and a single number does not fully describe map quality.
- Map and model validation are important to assess whether the map and model accurately represent the structure and are not affected by model bias.
This document provides an overview of a protein crystallography course taught by Robert Stroud. The course will cover:
1. Understanding crystallography and protein structures through an interactive laboratory course where students crystallize a protein and determine its structure.
2. Visiting the Advanced Light Source facility to collect X-ray diffraction data.
3. Key topics covered include crystal lattices, X-ray diffraction, determining atomic structures using X-ray crystallography, and solving the phase problem.
4. Resources provided include computing resources, structure determination software, and online courses and references.
K-Nearest Neighbor (KNN) is a supervised machine learning algorithm that can be used for classification and prediction. It finds the K closest training examples to a new data point and assigns the most common class among those K examples to the new data point. Euclidean distance is often used to calculate the distance between points. An example is provided of classifying a new paper sample as good or bad based on acid durability and strength attributes by finding its 3 nearest neighbors and assigning it the majority class.
This document discusses atom probe tomography (APT), a technique that analyzes the composition of materials at the atomic scale. APT works by applying voltage pulses or laser pulses to a needle-shaped sample, causing atoms on the surface to evaporate and fly towards a detector. Statistical analysis methods are then used to process the large datasets produced. These include analyzing nearest neighbor distances, clustering, pair correlation functions, Delaunay tessellation, and Voronoi cell distributions to identify clusters and assess randomness. The document reviews several key studies applying these statistical tools and techniques to APT data.
This document summarizes a distributed cloud-based genetic algorithm framework called TunUp for tuning the parameters of data clustering algorithms. TunUp integrates existing machine learning libraries and implements genetic algorithm techniques to tune parameters like K (number of clusters) and distance measures for K-means clustering. It evaluates internal clustering quality metrics on sample datasets and tunes parameters to optimize a chosen metric like AIC. The document outlines TunUp's features, describes how it implements genetic algorithms and parallelization, and concludes it is an open solution for clustering algorithm evaluation, validation and tuning.
My presentation in MST -11 International WorkshopArpit Gupta
1. The document presents a method for adaptive clutter rejection in atmospheric radars using direction of arrival (DOA) tracking with unscented filters. DOA estimation is performed using differential MUSIC and then updated over time using an unscented Kalman filter.
2. A simulation was conducted with four signal sources, one stationary and two moving, received by a radar array. DOA measurements from the unscented filter and MUSIC algorithm were used to reject clutter based on a constrained minimum power criterion.
3. The results demonstrate that the proposed method provides effective sidelobe cancellation for atmospheric radars through high resolution DOA estimation and state estimation of moving objects over time.
Ant colony search and heuristic techniques for optimal dispatch of energy sou...Beniamino Murgante
Ant colony search and heuristic techniques for optimal dispatch of energy sources in micro-grids - Eleonora Riva Sanseverino – University of Palermo (Italy)
Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)
Similar to Dog Breed Classification Using Part Localization (20)
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
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Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
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Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
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Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
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Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
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Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
1. Dog Breed Classification Using Part
Localization
Jiongxin Liu 1,
Angjoo Kanazawa2,
David Jacobs 2, and Peter Belhumeur1
1 Columbia University 2 University of Maryland
2. Fine-grained classification
[Branso
[Nilsback
n et al
and
‘10]
Zisserman
’08]
[Parkhi et
al ’12]
[Kumar et
al ‘12]
3. Related work
• Dense feature extraction:
– Mine discriminative region with random forests [Yao et al
’11]
– Multiple Kernel Learning [Nilsback and Zisserman ’08]
– Post-segmentation [Parkhi and Zisserman ’12]
• Pose-normalized appearance:
– Birdlets [Farrell et al ’11]
4. Related work
• Dense feature extraction:
– Mine discriminative region with random forests [Yao et al
’11]
Generic sampling of features
– Multiple Kernel Learning [Nilsback and Zisserman ’08]
contains more noise than useful
– Post-Segmentation [Parkhi and Zisserman ’12]
information
• Pose-normalizedfine-grained classification!
for appearance:
– Birdlets [Farrell et al ’11]
5. Same breed or not? NO!!
Entlebucher Mountain Dog Greater Swiss Mountain Dog
6. Key insight: Differences in common parts are
more informative
Entlebucher Mountain Dog Greater Swiss Mountain Dog
Localize parts based on a non-parameteric method by [Belhumeur et al ‘11]
13. Overview of the system
1. Face Detection 2. Part Detection 3. Feature Extraction and ear localization
4. One vs All classification
14. Pipeline 1: Dog Face Detection
Keep the 5
highest scoring
windows
15. Pipeline 2: Localize Parts
Part locations Detector responses
Idea: From the “fit” to K most
similar exemplars weighted by the
detector output,
take the most probable part
location
16. Review: Consensus of Exemplars
...
Local Part Detectors Exemplar Selection Part Localization
Slide from Neeraj Kumar
17. RANSAC-like Exemplar Selection
1. Repeat r times:
a. Choose random exemplar k
b. Choose 2 random modes of local detector outputs D={d i} on query
c. Find similarity transform t that aligns exemplar to these points
d. Evaluate match of all i face parts for this (k,t) pair:
n
Probability of this
configuration given P(Xk,t | D) = C Õ P(x i
k,t
i
|d ) Part detector probability
at this (aligned) location
i
detector outputs
e. Add (k,t) pair to list of possible exemplars, ranked by score
2. Take top M (k,t) pairs for determining global configuration
Slide from Neeraj Kumar
18. Final Part Localization
For each face part i:
a. Compute distribution of this part from all M aligned exemplars
b. For each of the top M aligned exemplars [(k,t) pairs]:
Multiply normalized local detector outputs with global distribution of part computed from
exemplars to get scores at each pixel location
c. Add all scores together to get final scores at each pixel and choose max
Slide from Neeraj Kumar
19. Pipeline 2: Localize Parts
Part locations Detector responses
Difference between current part
location and that of exemplar
From K most similar exemplars and the detector output,
take the most probable part location
20. Pipeline 3: Infer ears using detected parts
With r(=10) exemplars from each breed
21. Pipeline 3: Infer ears using detected parts
With r(=10) exemplars from each breed
This is a joint work with Jiongxin Liu, Peter Belhumeur, and David Jacobs.
in which instances from different classes share common parts but have wide variation in shape and appearance. Examples are identifying species of ..These problems lie between the two extremes of individuals such as face identification and basic-level categorizes such as caltech-256.Motivation:A vision system that can do things that humans aren’t very good atApplication for education, examples such as leafsnap) domain of automatic species identification, which is extremely useful for biodiversity studies and general education.. (success in the dog domain will certainly lead to further success in broaderIt is a very challenging problem to solve. We chose dogs as our test domain, (Highlight dogs)
Birdlet<-poselts, find 3D volumetric primitives & describe classes based on their variations. Our work is complementary to their work in that bidlets focuses on using large, articulated parts while we utilize parts describable at point locations. We also use a hierarchical approach in which first the face and the more rigid parts of the face are discovered and used to find class-specific parts such as ears.Built on top of the recent methods for visual object recognition, related work addresses the problem of fine-grained categorization mainly by mining discriminative features via randomized sampling, or with multiple kernel learning framework, or extracting dense features over a segmentated image.Most relevant to our approach is the work by Farrell et al which uses the poselet framework to localize the head and body of birds enabling part-based feature extraction.
Dense feature extraction is often very powerful for object recognition and general visual classification tasks. However, this is not the case for fine-grained categorization, since categories are so visually similar, many regions contain more noise than useful information, and such generic sampling can miss fine details that are needed for correct classificationIn this work, we argue and demonstrate that fine-grained classification can be significantly improved if the features are localized at corresponding object parts.There is a vast literature on face detection and localization parts of human faces. We localize parts of the dog face built on the consensus of models approach by Belhumeur et al, which originally is a non-parametric face parts detector
Here is an example that demonstrates this insight.
Subordinate categories such as dogs/leaves all share semantic parts (legs for charis, stem for leaves, ears for cats and dogs) and the differencces in those parts are more informative than generic sampling of features. These two dogs are of different breeds. The texture of their fur and the color distribution is strikingly similar. But in general, Entlebucher mountain dogs have a shorter snout and rounder nostrils, more pendant, v-shaped, flatter ears while Greater Swiss Mountain dogs have longer snout, nostils that cut to the side with a visible septum, a line between the nostrils, and folded ears that hang on the side of the head.In this work, we argue and demonstrate that fine-grained classification can be significantly improved if the features are localized at corresponding object parts.There is a vast literature on face detection and localization parts of human faces. We localize parts of the dog face built on the consensus of exemplars approach by Belhumeur et al, a non-parametric face parts detector. We extend their method to perform object classification, which has only been previously applied to part detection
-all the dogs face the camera, dog images are from the datasetWe chose dog breed identification as a test case to demonstrate our method.Dogs are an excellent domain for fine grained categorization. After humans, dogs are possibly the most photographed species (perhaps after cats) on the internet.Determination of dog breeds is a very challenging task, sharing many of the challenges seen in fine-grained classification, and success in this domain will certainly lead to further success in broader domain of automatic species identification, which is extremely useful for biodiversity studies and general education.Since we focus on localizing dog parts, we have annotated 8 parts of all dogs in our dataset. Parts are the 2 eyes, nose, ear tips, ear bases, and the top fo the head. Because we only look at these parts, all of the dogs in our dataset are facing the camera, but with varying poses, scale, and rotation, where detection of face parts is far from trivial task.Now I will go over the challenges in recognizing breeds of dogs from a single picture. The first challenge as you can see is that there are many classes. In this work we deal with 133 breeds of dogs.(As a sidenote, all of the pictures you see on these slides are images of dogs from our dataset)
Many subsets of dog breeds are quite similar in appearance.
On top of that, there is also great variation within breeds. These two factors make identification of breeds very challenging especially for humans without expert knowledge.(Try to go back to slide 7and point to Lakeland terrior)
They come in innumerable poses, considerably more than that of human faces
Dogs are very diverse in its visual appearance.
The geometry of their face is also very deformable, again way more than the deformation in human faces.Especially their ear tips: Breeds like beagles have hanging ears, whereas breeds like akita have pointy upright ears.(Also note how nose has greater DoF than the nose of humans like in this picture where the eyes and nose are almost colinear because dogs faces are more 3D (less flat) than ours)These factors make localization of parts very challenging.
Here is the overview of our pipeline: First we detect dog face, then localize three parts, extract features at those places to find most simlar exemplars to detect the rest of the face parts. Then using all the parts we do breed classification and here is a sample result. Green border indicates the correct breed.
We use a sliding window RBF-svm regressor to detect dog faces. Each window has eight SIFT descriptors indicated by these boxes, concatenated into a 1024-dimensional feature vector. We have experimented with a cascaded adaboost detector with Haar like features which works very well with human faces. Perhaps due to the extreme variability on geometry and appearance of dogs faces, the cascaded adaboost detector produced way too many false detections. For details please referr the paper.We keep the 10 highest scoring face detection window and generate hypothesis of part locations for each of them. We keep the face window with the highest score in the next step.
Want part loc that max. probability of that part loc given the detector responses.We want to empose geometric constraints to detector outputs, by combining low-level detectors with labled exemplars.Exe., help create conditional indpt between different parts since we assume that each part is generated by one of the exemplars, so we can re-write…We include the exemplars in the calculation of (1) and marginalized outIntuitively, K exemplars that are most similar to location of the modes of the detector output is selected. They are then transformed to fit the current query image. The P(delta) term is modeled as a 2D gaussian, and the difference between the current part and the exemplar gives how well the model fits the location p_i. We pick the part location that has the highest fit to all $K$ models weighted by the confidence of the detector output.To localize face parts, we first train sliding widnwo linear-SVM detectors for each dog part using a single SIFT feature. If we denote C as detector responses for parts in image I,And p^I denote the ground truth locations of the parts in the image, our goal is to compute (1).Using exemplar (labeled training samples) we can wirte the above for each ith part as (2)The t stands for similarity transformation of model $k$. The K models are selected by RANSAC like procedure. K=100?
Different approach to part detection compared to DPM, but basically they both do the same MAP esimationDPM enforces geometric constraints between parts by parameterizing deformation between connected partsCoE enfoces geometric constraints non-parametricly (although not latent, and part labels are necessary)
Want part loc that max. probability of that part loc given the detector responses.We want to empose geometric constraints to detector outputs, by combining low-level detectors with labled exemplars.Exe., help create conditional indpt between different parts since we assume that each part is generated by one of the exemplars, so we can re-write…We include the exemplars in the calculation of (1) and marginalized outIntuitively, K exemplars that are most similar to location of the modes of the detector output is selected. They are then transformed to fit the current query image. The P(delta) term is modeled as a 2D gaussian, and the difference between the current part and the exemplar gives how well the model fits the location p_i. We pick the part location that has the highest fit to all $K$ models weighted by the confidence of the detector output.To localize face parts, we first train sliding widnwo linear-SVM detectors for each dog part using a single SIFT feature. If we denote C as detector responses for parts in image I,And p^I denote the ground truth locations of the parts in the image, our goal is to compute (1).Using exemplar (labeled training samples) we can wirte the above for each ith part as (2)The t stands for similarity transformation of model $k$. The K models are selected by RANSAC like procedure. K=100?
Similarlly, we infer the ears also by extension of the consensus of models approach. The equations are demonstrated by the animation here.Assuming that the three parts detected in the stage before are accurate, from each breed, we find $R$ many closest exemplars. Do a similarity transform, and find the parts that are most probable.
Again we do this for each breed. The reason why we take this hierarchical approach to detect ears is because the geometry of ears is very breed dependent. So in the end we’ll have 133 hypothesis location of ears.R = 10.
Only 1440-dimensional feature vector (11 parts + kmeans)Finally, for each 133 part hypothesis, we extract sift features at those part locations concatenate it along with color histogram of the face window and send to a linear one vs all svm.One may wonder that we might be missing a lot of information from the body features or fur which is discriminative for dogs like dauschhound, but it is much harder to accurately localize dog parts because of their deformability and occlusion, and if two dogs are easily discriminated by their fur, those breeds have low similarity in apperances and they are easier to classify. The real problem is when features such as fur color and texture is very similar and not discrminative enough, and in these cases looking at the rest of the dog parts is not so useful. One of our contribution is that we get a very good result just by considering their faces.
Note the similarity between the query and the incorrect first guesses.
Look at the magenta curve: Our first guessachieves 67% accuracy and within the first 10 guesses we have achieve 93% accuracy.The green curve is using bag of word approach on the extracted dense SIFT features in a face detection window. Baseline method for object recognition.The cyan and blue are state of the art approaches used earlier for fine-grained categorization methods.The cyan curve uses LLC (locally constrained linear coding) to encode the dictionary for BoW, and blue uses MKL framework and it is extremely inefficient.The second roc curve shows quantative justification of our steps. The pink curve is the our proposed method, the red curve is if we only use the highest scoring face detection window. Note that we keep 10 Green curve shows how without part localization (features extracted on a grid within the face detection window) the accuracy is much lower.
Speaking of efficiency, our system runs in real time as we have an operating iphone application available in itunes now.
Bare layout for ECCV 2012 video preparation. You may submit the .pptx file, or use “File->Save and Send->Create a Video”.Remember: Author names and title will be added above the video by us.
Bare layout for ECCV 2012 video preparation. You may submit the .pptx file, or use “File->Save and Send->Create a Video”.Remember: Author names and title will be added above the video by us.
Bare layout for ECCV 2012 video preparation. You may submit the .pptx file, or use “File->Save and Send->Create a Video”.Remember: Author names and title will be added above the video by us.
Bare layout for ECCV 2012 video preparation. You may submit the .pptx file, or use “File->Save and Send->Create a Video”.Remember: Author names and title will be added above the video by us.
Bare layout for ECCV 2012 video preparation. You may submit the .pptx file, or use “File->Save and Send->Create a Video”.Remember: Author names and title will be added above the video by us.