Hierarchical Matching with Side Information for Image Classification

Slides:



Advertisements
Similar presentations
DONG XU, MEMBER, IEEE, AND SHIH-FU CHANG, FELLOW, IEEE Video Event Recognition Using Kernel Methods with Multilevel Temporal Alignment.
Advertisements

Foreground Focus: Finding Meaningful Features in Unlabeled Images Yong Jae Lee and Kristen Grauman University of Texas at Austin.
Ming-Ming Cheng 1 Ziming Zhang 2 Wen-Yan Lin 3 Philip H. S. Torr 1 1 Oxford University, 2 Boston University 3 Brookes Vision Group Training a generic objectness.
Efficiently searching for similar images (Kristen Grauman)
Multi-layer Orthogonal Codebook for Image Classification Presented by Xia Li.
CS395: Visual Recognition Spatial Pyramid Matching Heath Vinicombe The University of Texas at Austin 21 st September 2012.
Patch to the Future: Unsupervised Visual Prediction
1 Part 1: Classical Image Classification Methods Kai Yu Dept. of Media Analytics NEC Laboratories America Andrew Ng Computer Science Dept. Stanford University.
Ziming Zhang *, Ze-Nian Li, Mark Drew School of Computing Science, Simon Fraser University, Vancouver, B.C., Canada {zza27, li, Learning.
CS4670 / 5670: Computer Vision Bag-of-words models Noah Snavely Object
Bag-of-features models. Origin 1: Texture recognition Texture is characterized by the repetition of basic elements or textons For stochastic textures,
Global spatial layout: spatial pyramid matching Spatial weighting the features Beyond bags of features: Adding spatial information.
Large-Scale Object Recognition with Weak Supervision
Ghunhui Gu, Joseph J. Lim, Pablo Arbeláez, Jitendra Malik University of California at Berkeley Berkeley, CA
Bag of Features Approach: recent work, using geometric information.
Recognition using Regions CVPR Outline Introduction Overview of the Approach Experimental Results Conclusion.
Beyond bags of features: Adding spatial information Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba.
1 Image Recognition - I. Global appearance patterns Slides by K. Grauman, B. Leibe.
Lecture 28: Bag-of-words models
Graz University of Technology, AUSTRIA Institute for Computer Graphics and Vision Fast Visual Object Identification and Categorization Michael Grabner,
Generic Object Detection using Feature Maps Oscar Danielsson Stefan Carlsson
Beyond bags of features: Adding spatial information Many slides adapted from Fei-Fei Li, Rob Fergus, and Antonio Torralba.
Object Recognition: History and Overview Slides adapted from Fei-Fei Li, Rob Fergus, Antonio Torralba, and Jean Ponce.
Spatial Pyramid Pooling in Deep Convolutional
Student: Kylie Gorman Mentor: Yang Zhang COLOR-ATTRIBUTES- RELATED IMAGE RETRIEVAL.
On the Object Proposal Presented by Yao Lu
A String Matching Approach for Visual Retrieval and Classification Mei-Chen Yeh* and Kwang-Ting Cheng Learning-Based Multimedia Lab Department of Electrical.
Style-aware Mid-level Representation for Discovering Visual Connections in Space and Time Yong Jae Lee, Alexei A. Efros, and Martial Hebert Carnegie Mellon.
Review: Intro to recognition Recognition tasks Machine learning approach: training, testing, generalization Example classifiers Nearest neighbor Linear.
Bag-of-features models. Origin 1: Texture recognition Texture is characterized by the repetition of basic elements or textons For stochastic textures,
Real-time Action Recognition by Spatiotemporal Semantic and Structural Forest Tsz-Ho Yu, Tae-Kyun Kim and Roberto Cipolla Machine Intelligence Laboratory,
Bag-of-Words based Image Classification Joost van de Weijer.
Problem Statement A pair of images or videos in which one is close to the exact duplicate of the other, but different in conditions related to capture,
Nonparametric Part Transfer for Fine-grained Recognition Presenter Byungju Kim.
Bag-of-features models. Origin 1: Texture recognition Texture is characterized by the repetition of basic elements or textons For stochastic textures,
Svetlana Lazebnik, Cordelia Schmid, Jean Ponce
Yao, B., and Fei-fei, L. IEEE Transactions on PAMI(2012)
SVM-KNN Discriminative Nearest Neighbor Classification for Visual Category Recognition Hao Zhang, Alex Berg, Michael Maire, Jitendra Malik.
Group Sparse Coding Samy Bengio, Fernando Pereira, Yoram Singer, Dennis Strelow Google Mountain View, CA (NIPS2009) Presented by Miao Liu July
Reading Between The Lines: Object Localization Using Implicit Cues from Image Tags Sung Ju Hwang and Kristen Grauman University of Texas at Austin Jingnan.
A Codebook-Free and Annotation-free Approach for Fine-Grained Image Categorization Authors Bangpeng Yao et al. Presenter Hyung-seok Lee ( 이형석 ) CVPR 2012.
Deformable Part Model Presenter : Liu Changyu Advisor : Prof. Alex Hauptmann Interest : Multimedia Analysis April 11 st, 2013.
Bag-of-Words based Image Classification (week I) Joost van de Weijer.
Locality-constrained Linear Coding for Image Classification
Shiliang Zhang1, Qi Tian2, Gang Hua3, Qingming Huang4, Shipeng Li2 1Key Lab of Intelli. Info. Process., Inst. of Comput. Tech., CAS, Beijing , China.
Recognition Using Visual Phrases
A Multiresolution Symbolic Representation of Time Series Vasileios Megalooikonomou Qiang Wang Guo Li Christos Faloutsos Presented by Rui Li.
Object Recognition as Ranking Holistic Figure-Ground Hypotheses Fuxin Li and Joao Carreira and Cristian Sminchisescu 1.
Presented by David Lee 3/20/2006
Goggle Gist on the Google Phone A Content-based image retrieval system for the Google phone Manu Viswanathan Chin-Kai Chang Ji Hyun Moon.
NICTA SML Seminar, May 26, 2011 Modeling spatial layout for image classification Jakob Verbeek 1 Joint work with Josip Krapac 1 & Frédéric Jurie 2 1: LEAR.
Presented by David Lee 3/20/2006
Data Driven Attributes for Action Detection
Learning Mid-Level Features For Recognition
Nonparametric Semantic Segmentation
Paper Presentation: Shape and Matching
ICCV Hierarchical Part Matching for Fine-Grained Image Classification
Learning to Detect a Salient Object
Cheng-Ming Huang, Wen-Hung Liao Department of Computer Science
IEEE ICIP Feature Normalization for Part-Based Image Classification
CS 1674: Intro to Computer Vision Scene Recognition
CVPR 2014 Orientational Pyramid Matching for Recognizing Indoor Scenes
“The Truth About Cats And Dogs”
Brief Review of Recognition + Context
SIFT keypoint detection
Outline Background Motivation Proposed Model Experimental Results
FOCUS PRIOR ESTIMATION FOR SALIENT OBJECT DETECTION
Recognizing Deformable Shapes
Human-object interaction
Do Better ImageNet Models Transfer Better?
Presentation transcript:

Hierarchical Matching with Side Information for Image Classification CVPR 2012 Hierarchical Matching with Side Information for Image Classification Authors Qiang Chen et al. Presenter Hyung-seok Lee (이형석)

Motivation (1/2) Bag-of-Words (BoW) Frequency histogram of visual words Not emphasize any elements with regard to image layout

Motivation (2/2) Spatial Pyramid Matching (SPM) Partitioning the image plane into fine sub-regions Not optimum for object-centered recognition problem Beyond Bags of Features : Spatial Pyramid Matching for Recognizing Natural Scene Categories, Svetlana Lazebnik et al.

Main Idea Generalized Hierarchical Matching (GHM) Extends the popular matching work Integrate prior knowledge for enhancing feature matching side information

Overview Image Local features & side information Hierarchical clustering Hierarchical structure representation Matching over each cluster

Side Information (1/4) Object Confidence Map Object position should be extremely beneficial Shape model / Appearance model

Side Information (2/4) Shape-based object detection : HOG features Appearance-based object detection : BoW features Object Confidence Map

Side Information (3/4) Visual Saliency Map Aim to detect the important content in images Explain human visual search strategies SUN : A Bayesian Framework for Saliency Using Natural Statistics, Lingyun Zhang et el.

Side Information (4/4) Use the saliency model SUN (Saliency Using Natural statistics) Based on natural image statistics

Side Information Combination Combine object confidence map & visual saliency map Design a 3 level hierarchical structure - Object confidence map is used in level 2 - Background area will be further utilized in level 3

Encoding & Matching (1/2)

Encoding & Matching (2/2) Encoding is operated on each cluster Matching over each corresponding cluster

Experiments (1/2) Caltech-UCSD Birds 200 & Oxford Flowers 17 and 102

Experiments (2/2) VOC 2007 VOC 2010

Conclusion Propose the Generalized Hierarchical Matching framework - Extends the popular pyramid matching Two novel kinds of side information are introduced to enhance object-oriented image classification tasks Object confidence map Visual saliency amp