Homework 9 17-2011
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CrowdSearch is a system that uses human workers on Amazon Mechanical Turk to validate image search results in real-time on mobile phones. It has three main components: 1) an image search engine that extracts features from query images and searches a database, 2) a crowdsearch algorithm that optimizes delay and cost by predicting validation times and results, and 3) a system implementation that coordinates the image search engine and human validators. The system was able to improve search precision using human validation while meeting user-specified deadlines in experimental evaluations.
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like Euclidean distance algorithm or Mahalanobis Algorithm.
If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an
alarm signal is raised.
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)
The proposed work aims to create a smart application camera, with the intention of eliminating the need for a human presence to detect any unwanted sinister activities, such as theft in this
case. Spread among the campus, are certain valuable biometric identification systems at arbitrary locations. The application monitosr these systems (hereafter referred to as “object”)
using our smart camera system based on an OpenCV platform.
By using OpenCV Haar Training, employing the Viola-Jones algorithm implementation in OpenCV, we teach the machine to identify the object in environmental conditions. An added
feature of face recognition is based on Principal Component Analysis (PCA) to generate Eigen Faces and the test images are verified by using distance based algorithm against the eigenfaces, like Euclidean distance algorithm or Mahalanobis Algorithm. If the object is misplaced, or an unauthorized user is in the extreme vicinity of the object, an alarm signal is raised.
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...
In recent years, image retrieval process has increased artistically. An image retrieval system is a process for searching and retrieving images from large amount of the image dataset. Color, texture and edge have been the primitive low level image descriptors in content based image retrieval systems. In this paper we discover a system which splits the search process into two stages. In the query specify approach the feature descriptors of a query image we re-extracted and then used to check the similarity between the query image and those images which is in database. In the evolution stage, the most relevant images where retrieved by using the Interactive genetic algorithm. IGA help the users to retrieve the images that are most relevant to the users’ need and SVM will rank the image as their title and as par time of search. So that user can get search image as par their requirements.
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Captcha
The document presents a new CAPTCHA technique based on identifying an image's upright orientation. This task is difficult for computers but easy for humans. Candidate images are filtered to remove those that computers can automatically orient upright. A social feedback mechanism is then used to verify the remaining images have an orientation humans can recognize. Experiments showed this technique achieves high success rates for humans and low rates for bots, without requiring text entry.
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...
This paper presents the application of multi dimensional feature reduction of Consistency Subset Evaluator (CSE) and Principal Component Analysis (PCA) and Unsupervised Expectation Maximization (UEM) classifier for imaging surveillance system. Recently, research in image processing has raised much interest in the security surveillance systems community. Weapon detection is one of the greatest challenges facing by the community recently. In order to overcome this issue, application of the UEM classifier is performed to focus on the need of detecting dangerous weapons. However, CSE and PCA are used to explore the usefulness of each feature and reduce the multi dimensional features to simplified features with no underlying hidden structure. In this paper, we take advantage of the simplified features and classifier to categorize images object with the hope to detect dangerous weapons effectively. In order to validate the effectiveness of the UEM classifier, several classifiers are used to compare the overall accuracy of the system with the compliment from the features reduction of CSE and PCA. These unsupervised classifiers include Farthest First, Densitybased Clustering and k-Means methods. The final outcome of this research clearly indicates that UEM has the ability in improving the classification accuracy using the extracted features from the multi-dimensional feature reduction of CSE. Besides, it is also shown that PCA is able to speed-up the computational time with the reduced dimensionality of the features compromising the slight decrease of accuracy.
Enhancing The Human Perceivable Image Content In CAPTCHA Based BF Algorithm
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Tag based image retrieval (tbir) using automatic image annotation
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Tag based image retrieval (tbir) using automatic image annotation
Abstract In recent days, several social networking sites are more popular with digitized images. It comprises the major portion of the databases which makes the search engines to face difficulty in searching. We present a proficient image retrieval technique, which achieves eminent retrieval efficiency. Most of the images are annotated manually, thus the visual content and tags may be mismatched. This leads to poor performance in Tag Based Image Retrieval (TBIR). Automatic Image Annotation (AIA) analyzes the missing and noisy tags and over-refines it to increase the performance of TBIR. AIA can be achieved using the Tag Completion algorithm. The images retrieved from the TBIR are ranked based on the relevancy of the tags and visual content of the images. The relevancy can be evaluated using Content Based Image Retrieval (CBIR) technique. Based on the ranks, the images are indexed in the Tag matrix. Thus the images that match the search query can be retrieved in an optimal way. Keywords: Image Retrieval, Automatic Image Annotation, Tag Based Image Retrieval (TBIR), Tag Completion Algorithm, Content Based Image Retrieval (CBIR), Tag Matrix
IRJET- Smart Classroom Attendance System: Survey
1. The document discusses various techniques that have been proposed for face detection and attendance systems, including Haar classifiers, improved support vector machines, and local binary patterns algorithms.
2. It reviews several papers that have implemented different methods for face recognition for attendance systems, such as using HOG features and PCA for dimensionality reduction along with SVM classification.
3. The document also summarizes a paper that proposed a context-aware local binary feature learning method for face recognition that exploits contextual information between adjacent image bits.
IRJET - Explicit Content Detection using Faster R-CNN and SSD Mobilenet V2
This document compares two object detection models, Faster R-CNN and SSD MobileNet v2, for detecting explicit content in images. Faster R-CNN uses a region proposal network to identify regions of interest, which are then classified and bounded. SSD MobileNet combines the Single Shot Detector framework with the efficient MobileNet architecture using depthwise separable convolutions. The document evaluates these models in terms of speed, accuracy, and model size for explicit content detection.
Ijcet 06 10_004
This document describes a proposed content-based image retrieval system that uses histogram values and user feedback to effectively search and rank image search results. The proposed system tracks user navigation patterns and stores user feedback information separately from image data to improve search performance. Images are ranked based on their histogram values, user feedback, text metadata, and number of clicks to better capture user intent than existing keyword-based search systems. This approach aims to address challenges with existing CBIR systems such as helping users refine image queries and reducing information overload through improved query understanding and result ranking.
System analysis and design for multimedia retrieval systems
Due to the extensive use of information technology and the recent developments in multimedia systems, the
amount of multimedia data available to users has increased exponentially. Video is an example of
multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio.
Content based video retrieval is an approach for facilitating the searching and browsing of large
multimedia collections over WWW. In order to create an effective video retrieval system, visual perception
must be taken into account. We conjectured that a technique which employs multiple features for indexing
and retrieval would be more effective in the discrimination and search tasks of videos. In order to validate
this, content based indexing and retrieval systems were implemented using color histogram, Texture feature
(GLCM), edge density and motion..
A SURVEY ON CONTENT BASED IMAGE RETRIEVAL USING MACHINE LEARNING
This document provides a literature review of recent research on content-based image retrieval using machine learning techniques. It summarizes 8 research papers that used approaches like convolutional neural networks, color histograms, deep learning, hashing functions and more to extract image features and retrieve similar images from databases. The goal of content-based image retrieval is to find images that are semantically similar to a query image based on visual features.
COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION
Computer vision algorithms are essential components of many systems in operation today. Predicting the robustness of such algorithms for different visual distortions is a task which can
be approached with known image quality measures. We evaluate the impact of several image distortions on object segmentation, tracking and detection, and analyze the predictability of this impact given by image statistics, error parameters and image quality metrics. We observe that
existing image quality metrics have shortcomings when predicting the visual quality of virtual or augmented reality scenarios. These shortcomings can be overcome by integrating computer vision approaches into image quality metrics. We thus show that image quality metrics can be
used to predict the success of computer vision approaches, and computer vision can be employed to enhance the prediction capability of image quality metrics – a reciprocal relation.
Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation
Computer vision algorithms are essential components of many systems in operation today.
Predicting the robustness of such algorithms for different visual distortions is a task which can
be approached with known image quality measures. We evaluate the impact of several image
distortions on object segmentation, tracking and detection, and analyze the predictability of this
impact given by image statistics, error parameters and image quality metrics. We observe that
existing image quality metrics have shortcomings when predicting the visual quality of virtual
or augmented reality scenarios. These shortcomings can be overcome by integrating computer
vision approaches into image quality metrics. We thus show that image quality metrics can be
used to predict the success of computer vision approaches, and computer vision can be
employed to enhance the prediction capability of image quality metrics – a reciprocal relation.
IRJET- Comparative Analysis of Video Processing Object Detection
This document summarizes research on comparative analysis of video processing object detection techniques. It begins with an abstract describing the goal of object detection in images and videos and challenges involved. It then discusses benefits of object detection and provides a literature review summarizing the approaches of 15 other research papers on object detection, including approaches using background subtraction, segmentation, feature extraction and deep learning algorithms. The document concludes by stating that object detection has wide applications and research is ongoing to improve accuracy and robustness of detection.
Moving object detection using background subtraction algorithm using simulink
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Mri brain image retrieval using multi support vector machine classifier
This document discusses content-based image retrieval (CBIR) for medical images. It proposes using multiple query images instead of a single query image to improve retrieval accuracy. The system works by preprocessing queries, extracting features like texture from the queries, optimizing the features, using classifiers like SVM to categorize images, and then using KNN to retrieve similar images from the database based on feature matching. It claims this approach improves on existing CBIR systems that rely on annotations and have difficulties bridging the semantic gap between low-level features and high-level meanings.
Similar to Homework 9 17-2011 (20)
META-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVAL
META-HEURISTICS BASED ARF OPTIMIZATION FOR IMAGE RETRIEVAL
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)
AUTOMATIC THEFT SECURITY SYSTEM (SMART SURVEILLANCE CAMERA)
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...
An Enhance Image Retrieval of User Interest Using Query Specific Approach and...
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...
Multi-Dimensional Features Reduction of Consistency Subset Evaluator on Unsup...
Enhancing The Human Perceivable Image Content In CAPTCHA Based BF Algorithm
Enhancing The Human Perceivable Image Content In CAPTCHA Based BF Algorithm
Tag based image retrieval (tbir) using automatic image annotation
Tag based image retrieval (tbir) using automatic image annotation
Tag based image retrieval (tbir) using automatic image annotation
Tag based image retrieval (tbir) using automatic image annotation
IRJET - Explicit Content Detection using Faster R-CNN and SSD Mobilenet V2
IRJET - Explicit Content Detection using Faster R-CNN and SSD Mobilenet V2
System analysis and design for multimedia retrieval systems
System analysis and design for multimedia retrieval systems
A SURVEY ON CONTENT BASED IMAGE RETRIEVAL USING MACHINE LEARNING
A SURVEY ON CONTENT BASED IMAGE RETRIEVAL USING MACHINE LEARNING
COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION
COMPUTER VISION PERFORMANCE AND IMAGE QUALITY METRICS: A RECIPROCAL RELATION
Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation
Computer Vision Performance and Image Quality Metrics : A Reciprocal Relation
IRJET- Comparative Analysis of Video Processing Object Detection
IRJET- Comparative Analysis of Video Processing Object Detection
Moving object detection using background subtraction algorithm using simulink
Moving object detection using background subtraction algorithm using simulink
Mri brain image retrieval using multi support vector machine classifier
Mri brain image retrieval using multi support vector machine classifier
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Homework 9 17-2011
- 1. CrowdSearch: Exploiting Crowds for Accurate Real-time Image Search on Mobile Phones TienShengWen Department of Electronic Engineering Chung-Yuan Christian University, Taiwan Tingxin Yan, Vikas Kumar, Deepak Ganesan Department of Computer Science University of Massachusetts, Amherst, MA 01003 {yan, vikas, dganesan}@cs.umass.edu
- 3. System Architecture
- 6. Image Search Engine
- 7. System Implementation
- 8. Experimental Evaluation
- 16. Minimizing Human Bias and Error
- 19. Image Search Engine (2/2) A SeqTree to Predict Validation Results. The received sequence is ‘YNY’, the two sequences that lead to positive results are ‘YNYNY’ and ‘YNYY’. The probability that ‘YNYY’ occurs given receiving ‘YNY’ is 0.16/0.25 = 64%
- 20. System Implementation CrowdSearch Implementation Components Diagram
- 22. Improving Search Precision