Presentation is loading. Please wait.

Presentation is loading. Please wait.

Nonnegative Shared Subspace Learning and Its Application to Social Media Retrieval Presenter: Andy Lim.

Published byMoris Chambers Modified over 9 years ago

Similar presentations

Presentation on theme: "Nonnegative Shared Subspace Learning and Its Application to Social Media Retrieval Presenter: Andy Lim."— Presentation transcript:

1 Nonnegative Shared Subspace Learning and Its Application to Social Media Retrieval Presenter: Andy Lim

2 Paper Topic Folksonomy Social media sharing platforms

3 The Problem Rise in popularity of social image and video sharing platforms Precision of tag-based media retrieval Tags are Noisy Ambiguous Incomplete Subjective Lack of constraints Free-text tags (i.e. “djfja;sldfkj”) Tags: hotdog, chinese, trololol, aidjishi, sandwich, bread

4 Previous Research (Internal) Improving tag relevance Sigurbjornsson and Zwol Developed a method of recommending a set of relevant tags based on tag popularity Li et al. List all images for a given tag and determine tag relevance from visual similarity All are confined to noisy tags within the primary dataset

5 The Approach Internal vs. External Leverage external auxiliary sources of information to improve target tagging systems (presumably much noisier) Exploit disparate characteristics of target domain using auxiliary source Note: What is the optimal level of joint modeling such that the target domain still benefits from the auxiliary source?

6 Assumptions There is a common underlying subspace shared by the primary and secondary domains The primary domain is much nosier than the secondary domains

7 Nonnegative Matrix Factorization X (M x N data matrix) where N = documents in terms of M vocabulary words F (M x R nonnegative matrix) represents R basis vectors H (R x N nonnegative matrix) contains coordinates of each document

8 Joint Shared Nonnegative Matrix Factorization (JSNMF) Input: X (target domain), Y (auxiliary domain), R 1 and R 2 (dimensionality of underlying subspaces of X and Y), K (basis vectors) Output: W (joint shared subspace), U (remaining subspace in target domain), V (remaining subspace in auxiliary domain), H (coordinate matrix for target domain), L (coordinate matrix for auxiliary domain)

9 Retrieval using JSNMF Input: W, U, H, query sentence S Q, number of images (or videos) to be retrieved N and image (or video) dataset Output: Return top N retrieved images (or videos)

10 Experiment Use LabelMe tags (auxiliary) to improve Image retrieval in Flickr Video retrieval in Youtube Why LabelMe? Object image tagging Controlled vocabulary

11 Flickr Dataset Downloaded 50,000 images from Flickr Average number of distinct tags = 8 Removed Rare tags (appears less than 5 times) Images with no tags and non-English tags Obtained 20,000 labeled images 7,000 examples are kept for investigating internal auxiliary dataset

12 YouTube Dataset Downloaded 18,000 videos’ metadata (tags, URL, category, title, comments, etc.) Average number of distinct tags = 7 Removed Rare tags (appearing less than 2 times) Videos with no tags or non-English tags Obtained dataset corresponding to 12,000 videos Again, kept 7,000 examples to be used as an internal auxiliary dataset

13 LabelMe Dataset Added 7,000 images with tags from LabelMe Average number of distinct tags = 32 Removed Rare tags (appearing less than 2 times) Cleanup does not reduce dataset

14 Evaluation Measures Defined query set Q {cloud, man, street, water, road, leg, table, plant, girl, drawer, lamp, bed, cable, bus, pole, laptop, plate, kitchen, river, pool, flower} Manually annotated the two datasets (Flickr and YouTube) with respect to the query set (no benchmark dataset available) Query term and an image is relevant if the concept is clearly visible in the image (or video)

15 Results with JSNMF Precision-Scope Curve Fix recall at 0.1 Users are usually only interested in first few results 10% improvement

16 Results with JSNMF Under-representation Shares very few basis vectors Over-representation Forces many basis vectors to represent both datasets Appropriate level of representation

17 Flickr Retrieval Results Results are better with LabelMe As recall increases, precision decreases When K=0 (no sharing) or K=40 (fully sharing), precision is lower compared to K=15

18 YouTube Retrieval Results Similar to Flickr Results

19 Extra Notes & Questions? Can be extended to multiple datasets (not just 2) Can use generic model to apply to other data mining tasks

Download ppt "Nonnegative Shared Subspace Learning and Its Application to Social Media Retrieval Presenter: Andy Lim."

Similar presentations

A Comparison of Implicit and Explicit Links for Web Page Classification Dou Shen 1 Jian-Tao Sun 2 Qiang Yang 1 Zheng Chen 2 1 Department of Computer Science.

A Comparison of Implicit and Explicit Links for Web Page Classification Dou Shen 1 Jian-Tao Sun 2 Qiang Yang 1 Zheng Chen 2 1 Department of Computer Science.
Using Large-Scale Web Data to Facilitate Textual Query Based Retrieval of Consumer Photos.

Using Large-Scale Web Data to Facilitate Textual Query Based Retrieval of Consumer Photos.
Yansong Feng and Mirella Lapata

Yansong Feng and Mirella Lapata
Location Recognition Given: A query image A database of images with known locations Two types of approaches: Direct matching: directly match image features.

Location Recognition Given: A query image A database of images with known locations Two types of approaches: Direct matching: directly match image features.
Multimedia Database Systems

Multimedia Database Systems
Diversified Retrieval as Structured Prediction Redundancy, Diversity, and Interdependent Document Relevance (IDR ’09) SIGIR 2009 Workshop Yisong Yue Cornell.

Diversified Retrieval as Structured Prediction Redundancy, Diversity, and Interdependent Document Relevance (IDR ’09) SIGIR 2009 Workshop Yisong Yue Cornell.
1 Evaluation Rong Jin. 2 Evaluation  Evaluation is key to building effective and efficient search engines usually carried out in controlled experiments.

1 Evaluation Rong Jin. 2 Evaluation  Evaluation is key to building effective and efficient search engines usually carried out in controlled experiments.
GENERATING AUTOMATIC SEMANTIC ANNOTATIONS FOR RESEARCH DATASETS AYUSH SINGHAL AND JAIDEEP SRIVASTAVA CS DEPT., UNIVERSITY OF MINNESOTA, MN, USA.

GENERATING AUTOMATIC SEMANTIC ANNOTATIONS FOR RESEARCH DATASETS AYUSH SINGHAL AND JAIDEEP SRIVASTAVA CS DEPT., UNIVERSITY OF MINNESOTA, MN, USA.
Precision and Recall.

Precision and Recall.
Evaluating Search Engine

Evaluating Search Engine
Hinrich Schütze and Christina Lioma

Hinrich Schütze and Christina Lioma
 Users annotate things (resources) with labels (tags)  These annotations are shared, creating a collaborative dataset called a folksonomy coffee java.

 Users annotate things (resources) with labels (tags)  These annotations are shared, creating a collaborative dataset called a folksonomy coffee java.
Automatic Image Annotation and Retrieval using Cross-Media Relevance Models J. Jeon, V. Lavrenko and R. Manmathat Computer Science Department University.

Automatic Image Annotation and Retrieval using Cross-Media Relevance Models J. Jeon, V. Lavrenko and R. Manmathat Computer Science Department University.
1 Statistical correlation analysis in image retrieval Reporter : Erica Li 2004/9/30.

1 Statistical correlation analysis in image retrieval Reporter : Erica Li 2004/9/30.
Commentary-based Video Categorization and Concept Discovery By Janice Leung.

Commentary-based Video Categorization and Concept Discovery By Janice Leung.
Reference Collections: Task Characteristics. TREC Collection Text REtrieval Conference (TREC) –sponsored by NIST and DARPA (1992-?) Comparing approaches.

Reference Collections: Task Characteristics. TREC Collection Text REtrieval Conference (TREC) –sponsored by NIST and DARPA (1992-?) Comparing approaches.
MANISHA VERMA, VASUDEVA VARMA PATENT SEARCH USING IPC CLASSIFICATION VECTORS.

MANISHA VERMA, VASUDEVA VARMA PATENT SEARCH USING IPC CLASSIFICATION VECTORS.
Probabilistic Latent Semantic Analysis

Probabilistic Latent Semantic Analysis
Introduction to Machine Learning Approach Lecture 5.

Introduction to Machine Learning Approach Lecture 5.
Jinhui Tang †, Shuicheng Yan †, Richang Hong †, Guo-Jun Qi ‡, Tat-Seng Chua † † National University of Singapore ‡ University of Illinois at Urbana-Champaign.

Jinhui Tang †, Shuicheng Yan †, Richang Hong †, Guo-Jun Qi ‡, Tat-Seng Chua † † National University of Singapore ‡ University of Illinois at Urbana-Champaign.

Similar presentations

Ads by Google