Labelme: a Database and Web-Based Tool for Image Annotation Bryan C
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Learning and Using Taxonomies for Visual and Olfactory Category Recognition
Learning and Using Taxonomies for Visual and Olfactory Category Recognition Thesis by Greg Griffin In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy California Institute of Technology Pasadena, California 2013 (Defended March 2013) ii c 2013 Greg Griffin All Rights Reserved iii For Leslie iv v Acknowledgements vi vii Abstract Dominant techniques for image classification over the last decade have mostly followed a three-step process. In the first step, local features (such as SIFT) are extracted. In the second step, some statistic is generated which compares the features in each new image to the features in a set of training images. Finally, standard learning models (such as SVMs) use these statistics to decide which class the image is most likely to belong to. Popular models such as Spatial Pyramid Matching and Naive Bayse Nearest Neighbors adhere to the same general linear 3-step process even while they differ in their choice of statistic and learning model. In this thesis I demonstrate a hierarhical approach which allows the above 3 steps to in- teract more efficiently. Just as a child playing “20 questions” seeks to identify the unknown object with the fewest number of querries, our learning model seeks to classify unlabelled data with a minimial set of feature querries. The more costly feature extraction is, the more beneficial this approach can be. We apply this approach to three different applications: photography, microscopy and olfaction. Image —. Microscope slide —. Finally, — both efficiency and accuracy. In our tests, the resulting system functions well under real-world outdoor conditions and can recognize a wide variety of real-world odors. -
CS 558: Computer Vision 9Th Set of Notes
1 CS 558: Computer Vision 9th Set of Notes Instructor: Philippos Mordohai Webpage: www.cs.stevens.edu/~mordohai E-mail: [email protected] Office: Lieb 215 Introduction to object recognition By Svetlana Lazebnik Slides adapted from Fei-Fei Li, Rob Fergus, Antonio Torralba, and Jean Ponce Overview • Basic recognition tasks • A machine learning approach • Example features • Example classifiers • Levels of supervision • Datasets • Current trends and advanced recognition tasks Specific recognition tasks Scene categorization • outdoor/indoor • city/forest/factory/etc. Image annotation/tagging • street • people • building • mountain • … Object detection • find pedestrians Activity recognition • walking • shopping • rolling a cart • sitting • talking • … Image parsing sky mountain building tree building banner street lamp market people Image understanding? How many visual object categories are there? Biederman 1987 OBJECTS ANIMALS PLANTS INANIMATE NATURAL MAN-MADE ….. VERTEBRATE MAMMALS BIRDS TAPIR BOAR GROUSE CAMERA Recognition: A machine learning approach Slides adapted from Fei-Fei Li, Rob Fergus, Antonio Torralba, Kristen Grauman, and Derek Hoiem The machine learning framework • Apply a prediction function to a feature representation of the image to get the desired output: f( ) = “apple” f( ) = “tomato” f( ) = “cow” The machine learning framework y = f(x) output prediction function Image feature • Training: given a training set of labeled examples {(x1,y1), …, (xN,yN)}, estimate the prediction function f by minimizing the prediction error on the training set • Testing: apply f to a never before seen test example x and output the predicted value y = f(x) Steps Training Training Labels Training Images Image Learned Training Features model Learned model Testing Image Prediction Features Test Image Slide credit: D. -
Visual Object Category Recognition
Visual Object Category Recognition Robotics Research Group Department of Engineering Science University of Oxford Supervisors: Professor Andrew Zisserman Professor Pietro Perona Robert Fergus New College December 2, 2005 Abstract We investigate two generative probabilistic models for category-level object recognition. Both schemes are designed to learn categories with a minimum of supervision, requiring only a set of images known to contain the target category from a similar viewpoint. In both methods, learning is translation and scale-invariant; does not require alignment or correspondence between the training images, and is robust to clutter and occlusion. The schemes are also robust to heavy contamination of the training set with unrelated images, enabling them to learn directly from the output of Internet Image Search engines. In the first approach, category models are probabilistic constellations of parts, and their parameters are estimated by maximizing the likelihood of the training data. The appearance of the parts, as well as their mutual position, relative scale and probability of detection are explicitly represented. Recognition takes place in two stages. First, a feature-finder identifies promising locations for the model’s parts. Second, the category model is used to compare the likelihood that the observed features are generated by the category model, or are generated by background clutter. The second approach is a visual adaptation of “bag of words” models used to extract topics from a text corpus. We extend the approach to incorporate spatial information in a scale and translation-invariant manner. The model represents each image as a joint histogram of visual word occurrences and their locations, relative to a latent reference frame of the object(s). -
Dataset Issues in Object Recognition
Dataset Issues in Object Recognition J. Ponce1,2,T.L.Berg3, M. Everingham4,D.A.Forsyth1,M.Hebert5, S. Lazebnik1,M.Marszalek6,C.Schmid6, B.C. Russell7,A.Torralba7, C.K.I. Williams8, J. Zhang6, and A. Zisserman4 1 University of Illinois at Urbana-Champaign, USA 2 Ecole Normale Sup´erieure, Paris, France 3 University of California at Berkeley, USA 4 Oxford University, UK 5 Carnegie Mellon University, Pittsburgh, USA 6 INRIA Rhˆone-Alpes, Grenoble, France 7 MIT, Cambridge, USA 8 University of Edinburgh, Edinburgh, UK Abstract. Appropriate datasets are required at all stages of object recognition research, including learning visual models of object and scene categories, detecting and localizing instances of these models in im- ages, and evaluating the performance of recognition algorithms. Current datasets are lacking in several respects, and this paper discusses some of the lessons learned from existing efforts, as well as innovative ways to obtain very large and diverse annotated datasets. It also suggests a few criteria for gathering future datasets. 1 Introduction Image databases are an essential element of object recognition research. They are required for learning visual object models and for testing the performance of classification, detection, and localization algorithms. In fact, publicly available image collections such as UIUC [1], Caltech 4 [10], and Caltech 101 [9] have played a key role in the recent resurgence of category-level recognition research, driving the field by providing a common ground for algorithm development and evaluation. -
Labelme: a Database and Web-Based Tool for Image Annotation
MIT AI LAB MEMO AIM-2005-025, SEPTEMBER, 2005. LabelMe: a database and web-based tool for image annotation BRYAN C. RUSSELL, ANTONIO TORRALBA, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA [email protected], [email protected] KEVIN P. MURPHY Departments of computer science and statistics, University of British Columbia, Vancouver, BC V6T 1Z4 [email protected] WILLIAM T. FREEMAN Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA [email protected] Abstract Research in object detection and recognition in cluttered scenes requires large im- age collections with ground truth labels. The labels should provide information about the object classes present in each image, as well as their shape and locations, and possibly other attributes such as pose. Such data is useful for testing, as well as for supervised learning. This project provides a web-based annotation tool that makes it easy to annotate images, and to instantly share such annotations with the community. This tool, plus an initial set of 10,000 images (3000 of which have been labeled), can be found at http://www.csail.mit.edu/»brussell/research/LabelMe/intro.html 1 Introduction Detecting generic object categories in natural, cluttered images is one of the holy grails of computer vision. In order to make progress towards this goal, it may be essential to have large databases of challenging images, in which “ground truth” labels are made publically available. These labels should provide information about the object classes present in each image, as well as their shape and locations, and possibly other attributes 1 such as pose. -
Topics of the Class
Advanced Topics in Computer Vision" and Robotics " ! Jana Kosecka! http://cs.gmu.edu/~kosecka/cs884/! [email protected]! ! ! ! Some slides thanks to S. Lazebnik, T. Berg, Fei-Fei Li, K. Grauman and others! Topics of the class! •# State of the art of scene understanding! •# Object, Scene, Human Activity Recognition ! ! With applications to: ! ! •# Image Based Retrieval! •# Image tagging ! •# Robot Perception, environment understanding ! 1! Logistics! •# Grading: Homeworks, Presentations, Class Participation 60% Final exam/ project: 40% ! •# Prerequisites: Computer Vision, Robotics, AI, Data Mining, Pattern Recognition! •# Related Resources: Material covered in CS682, CS685 and textbooks and ! recommended materials there! •# Lectures: Introduction by an instructor, 3 paper presentations per class, discussions, each student will present one paper every second week; all students should read all papers to participate in discussion; programming homeworks every second week ! •# Projects: up to teams of 2 people! •# Dates! –# Project proposals due! –# May week of finals final report due! –# Project presentations! •# Required Software MATLAB (with Image Processing toolbox)! •# Open CV library! Student Participation, Presentation! •# 2-3 papers for each week discussing selected topic! •# 10-15 minute presentation of one paper with slides! •# Discuss the main idea of the paper! •# The methods used, if the code is available demonstrate the method! •# Provide opinion, compare related to the other papers on the same topic! •# Presenter should stimulate -
LNCS 4170, Pp
Dataset Issues in Object Recognition J. Ponce1,2,T.L.Berg3, M. Everingham4,D.A.Forsyth1,M.Hebert5, S. Lazebnik1,M.Marszalek6,C.Schmid6, B.C. Russell7,A.Torralba7, C.K.I. Williams8, J. Zhang6, and A. Zisserman4 1 University of Illinois at Urbana-Champaign, USA 2 Ecole Normale Sup´erieure, Paris, France 3 University of California at Berkeley, USA 4 Oxford University, UK 5 Carnegie Mellon University, Pittsburgh, USA 6 INRIA Rhˆone-Alpes, Grenoble, France 7 MIT, Cambridge, USA 8 University of Edinburgh, Edinburgh, UK Abstract. Appropriate datasets are required at all stages of object recognition research, including learning visual models of object and scene categories, detecting and localizing instances of these models in im- ages, and evaluating the performance of recognition algorithms. Current datasets are lacking in several respects, and this paper discusses some of the lessons learned from existing efforts, as well as innovative ways to obtain very large and diverse annotated datasets. It also suggests a few criteria for gathering future datasets. 1 Introduction Image databases are an essential element of object recognition research. They are required for learning visual object models and for testing the performance of classification, detection, and localization algorithms. In fact, publicly available image collections such as UIUC [1], Caltech 4 [10], and Caltech 101 [9] have played a key role in the recent resurgence of category-level recognition research, driving the field by providing a common ground for algorithm development and evaluation.