Bridge Semantic Gap A Large Scale Concept Ontology

Bridge Semantic Gap: A Large Scale Concept Ontology for Multimedia (LSCOM) Guo-Jun Qi Beckman Institute University of Illinois at Urbana-Champaign

LSCOM (Large Scale Concept Ontology for Multimedia) A broadcast news video dataset 200+ news videos/ 170 hours 61, 901 shots Language ◦ English/Arabic/Chinese

Why broadcast News ontology? Critical mass of users, content providers, applications Good content availability (TRECVID LDC FBIS) Share Large set of core concepts with other domains

LSCOM Provides Richly annotated video content for accomplishing required access and analysis functions over massive amount of video content Large scale useful well-defined semantic lexicon ◦ More than 3000 concepts ◦ 374 annotated concepts ◦ Bridging semantic gap from low-level features to high-level concepts

A LSCOM concept 000 - Parade Concept ID: 000 Name: Parade Definition: Multiple units of marchers, devices, bands, banners or Music. Labeled: Yes

LSCOM Hierarchy http: //www. lscom. org/ontology/index. html Thing. Individual. . Dangerous_Thing. . . Dangerous_Situation. . Emergency_Incident. . . Disaster_Event. . . Natural_Disaster. . Natural_Hazard. . . Avalance. . . Earthquake. . . Mudslide. . . Natural_Disaster. . . Tornado. . . Dangerous_Tangible_Thing. . Cutting_Device

Definition: What’s the ontology? (Wikipedia) An ontology is a formal representation of the knowledge by a set of concepts within a domain and the relationships between those concepts. It is used to reason about the properties of that domain, and may be used to describe the domain.

Ontology Represents the visual knowledge base in a structure way ◦ Graph structure ◦ Tree (hierarchy) structure Images/videos can be effectively learned and retrieved by the coherence between concepts ◦ Logical coherence ◦ Statistical coherence

An Ontology Hierarchy: Military Vehicle

An example from Wikipedia

Ontology Tree for LSCOM

A Light Scale Concept Ontology for Multimedia Understanding (LSCOM-Lite) The aim is to break the semantic space using a few concepts (39 concepts). Selection Criteria ◦ Semantic Coverage As many as semantic concepts in News videos could be covered by the light concept set. ◦ Compactness These concept should not semantically overlap. ◦ Modelability These concepts could be modeled with a smaller semantic gap.

Selected concept dimensions Divide the semantic space into a multimedia-dimensional space, where each dimension is nearly orthogonal ◦ ◦ ◦ ◦ Program Category Setting/Scene/Site People Objects Activities Events Graphics

Histogram of LSCOM-Lite Concepts

Some example keyframes

Applications Application I: Conceptual Fusion (most basic – early fusion) Application II: Cross-Category Classification (inter-class relation) Application III: Event Dynamic in Concept Space

Application I: Conceptual Fusion Concept 1 Concept 2 Video Concept 3 … Concept n Visual Features Classifie r

LSCOM 374 Models 374 LIBSVM models ◦ http: //www. ee. columbia. edu/ln/dvmm/colu mbia 374/ ◦ Feature used (MPEG-7 descriptors) Color Moments Edge Histogram Wavelet Texture ◦ LIBSVM – a library for support vector machine at http: //www. csie. ntu. edu. tw/~cjlin/libsvm/

Application II: cross-category classification with concept transfer G. -J. Qi et al. Towards Cross-Category Knowledge Propagation for Learning Visual Concepts, in CVPR 2011

Instance-Level Concept Correlation Mountain Castle +1 l ast C -1 +1 Mountain and castle nly o e M ou nta in on ly -1

Transfer Function Mountain, Castle Mountain Castle None of them

Model Concept Relations

Automatically construct ontology in a data-driven manner

An application III – Event Dynamics in Concept Space

Event Detection with Concept Dynamics W. Jiang et al, Semantic event detection based on visual concept prediction, ICME, Germany, 2008.

Open Problems Cross-Dataset Gap ◦ Generalize LSCOM dataset to other dataset (e. g. , non-news video dataset) Cross-Domain Gap ◦ Text script associated with news videos Can help information extraction for visual concepts? Automatic ontology construction ◦ Task dependent v. s. task independent ◦ Data driven v. s. preliminary knowledge (e. g. , Word. Net) ◦ Incorporate prior human knowledge (logic relation etc. )

TRECVID Competition Task 1: High-Level Feature Extraction ◦ Input: subshot ◦ Output: detection results for 39 LSCOMLite concepts in the subshot

High-Level Feature Extraction Each concept assumed to be binary (absent or present) in each subshot Submission: Find subshots that contain a certain concept, rank them by the detection confidence score, and submit the top 2000. Evaluations: NIST evaluated 20 medium frequent concepts from 39 concepts using a 50% random samples of all the submission pools

20 Evaluated Concepts

Evaluation Metric: Average Precision Relevant subshots should be ranked higher than the irrelevant ones. R is the number of relevant images in total, Rj is the number of relevant images in top j images, Ij indicates if the jth image is irrelevant or not.

Results

TRECVID Competition Task II: Video Search ◦ Input: text-based 24 topics ◦ Output: relevant subshots in the database

Topics to search

Topics to search (cont’d)

Topics to search

Three Types of Search Systems

Results: Automatic Runs

Results: Manual Runs

Results: Interactive Runs

Machine Problem 7: Shot Boundary Detection in Videos

Goals Detect the abrupt content changes between consecutive frames. ◦ Scene changes ◦ Scene cuts

Steps Step 1: Measuring the change of content between video frames ◦ Visual/Acoustic measurements Step 2: Compare the content distance between successive frames. If the distance is larger than a certain threshold, then a shot boundary may exist.

Measuring Content based on Visual Information 256 dimensional Color Histogram ◦ In RGB space, normalize the r, g, b in [0, 1] ◦ Color nr space 8 X 8 histogram ng

Color Histograms Divide each image into four parts, each part has a 8 X 8 histogram, and 256 dim features in total.

Acoustic Features 12 cepstral coefficients Energy Zero (sum of square of raw signals) crossing rates (ZCR) ZCR = sum(|sign(S(2: N))-sign(S(1: N 1))|) Hints: normalize energy to avoid it over-dominating when computing distances between successive frames

Datasets Two videos of little over one minute Manually label the shot boundary

What to submit Source code Report ◦ compare shot boundary detection results returned by your algorithm with the manually labeled boundaries ◦ Compare ◦ Explain your choice of threshold ◦ Explain the differences between the acoustic-based and visual-based detection results

Where and when to submit Email to ece. 417@gmail. com Due: May 2 nd

Thanks! Q&A