RIAO 20042 video retrieval systems

The Físchlár-News-Stories System: Personalised Access to

an Archive of TV News

Alan F. Smeaton, Cathal Gurrin, Howon Lee, Kieran McDonald, Noel Murphy, Noel E. O’Connor, D

avid WilsonCentre for Digital Video Processing, Dublin City University

Derry O’Sullivan, Barry SmythSmart Media Institute, Department of Computer Science, U

niversity College Dublin

Introduction

• Físchlár systems– A family of tools for capturing, analysis, indexi

ng, browsing, searching and summarisation of digital video information

– Físchlár-News-Stories• Provides access to a growing archive of broadcast

TV news• Segment news into shots and stories, calendar loo

kup, text search, link between related stories, personalisation and recommend stories

System overview

Shot boundary detection

• Shot– A single camera motion in time– We can have camera movement as well as object

motion

• Shot cut– Hard cut– Gradual transition (GT)

• Boundary detection (Browne, et al., 2000)– Frame-frame similarity over a window of frames– Evaluation: TRECVID 2001

• Over 90% precision and recall for hard cuts• Somewhat less for GT

Story segmentation

• Cluster all keyframes of shots– Similarity: colour and edge histograms (O’Connor, et

al., 2001)

• Anchorperson shots– One of the clusters will have an average keyframe-ke

yframe similarity much higher than the others and this will most likely be a cluster of anchorperson shots

• Beginning of news, beginning/end of advertisement– Apply a speech-music discrimination algorithm to the

audio

Story segmentation

• Detect individual advertisements– Sadlier, et al., 2002

• Shot length– Outside broadcasts an d footage video tends to have

shorter shot lengths than the in-studio broadcasts• Using SVM to determine story bounds

– Combine the output of these analyses• Evaluation (TRECVID 2003)

– 31% recall and 45% precision• For the present time, the automatic segmentatio

n is manually checked for accuracy every day

Search based on text

• Closed captions– Typing error, omit phrases or sentences– Time lagging

• Retrieval– Simple IR engine– When a story’s detail is displayed, we use the

closed caption text from that story as a query against the closed caption archive and display summaries of the 10 top-ranked stories

Personalisation

• User feedback– Rating on a given news story using a 5-point

scale– These ratings are used as input to a

collaborative filtering system which can recommend news stories to users based on ratings from other users

• Need to recommend on new content• User vs. stories ratings matrix is very sparse

– Story-story similarity + user-story ratings

CIMWOS: A Multimedia Retrieval System based on Combined

Text, Speech and Image Processing

Harris Papageorgiou1, Prokopis Prokopidis1,

2, Iason Demiros1,2, Nikos Hatzigeorgiou1, George Carayannis1,2

1Institute for Language and Speech Processing2National Technical University of Athens

Introduction

• CIMWOS– Multimedia, multimodal and multilingual– Content-based indexing, archiving, retrieval and on-

demand delivery of audiovisual content– Video library

• Sports, broadcast news and documentaries in English, French and Greek

– Combine speech, language and image understanding technology

– Producing XML metadata annotations following the MPEG-7 standard

Speech processing Subsystem

• Speaker Change Detection (SCD)

• Automatic Speech Recognition (ASR)

• Speaker Identification (SID)

• Speaker Clustering (SC)

Text processing Subsystem

• Named Entity Detection (NED)

• Term Extraction (TE)

• Story Segmentation (SD)

• Topic Detection (TD)

Speech Transcriptions

Named Entity

Detection

Term Extraction

Story Segmentation

Topic Detection

XML

Text processing Subsystem

• Applied on the textual data produced by the Speech Processing Subsystem

• Named entity detection– sentence boundary identification– POS tagging– NED

• Lookup modules that match lists of NEs and trigger-words against the text, hand-crafted and automatically generated pattern grammars, maximum entropy modeling, HMM models, decision-tree techniques, SVM classifier, etc.

– Term extraction• Identify single or multi-word indicative keywords• Linguistic processing is performed through an augmented ter

m grammar, the results of which are statistically filtered using frequency-based scores

Text processing Subsystem

• Story detection and topic classification– Employ the same set of models– Generative, mixture-based HMM

• One state per topic, one state modeling general language, that is words not specific to any topic

• Each state models a distribution of words given the particular topic

• Running the resulting models on a sliding window, thereby noting the change in topic-specific words as the window moves on

Image processing Subsystem

• Automatic Video Segmentation (AVS)– Shotcut detection and keyframe extraction– Measurement of differences between consecutive fra

mes– Adaptive thresholding on motion and texture cues

• Face Detection (FD) and Identification (FI)– Locate faces in video sequences, and associate these

faces with names– Based on SVM

Image processing Subsystem

• Object Recognition (OR)– The object’s surface is decomposed in a large

number of regions– The spatial and temporal relationships of these

regions are acquired from several example views

• Video Text Detection and Recognition (TDR)– OCR

• Text detection• Text verification• Text segmentation• OCR

Integration

• All processing modules in the corresponding three modalities converge to a textual XML metadata annotation scheme following the MPEG-7 descriptors

• These XML metadata annotations are further processed, merged and loaded into CIMWOS Multimedia database

Indexing and retrieval

• Weighted Boolean model– Weight of index term: tf*idf– Image processing metadata are not weighted

• Two-step– 1. Boolean exact-match

• Objects, topics and faces

– 2. Query best-match• Text, terms and named entities

• Basic retrieval unit– passage

22

2expression Term WeightedDice

ii

ii

yx

yx

YX

YX

2

expressionBinary Dice

Indexing schema

episode

Story(ies)

Passage(s) Shot(s) Subshot(s)

Key-frame

Face(s) Object(s)

Text(s)Named entity(ies)

Term(s)

Word(s)

Speaker(s)

Topic(s)

Evaluation

• Greek news broadcasts– 35.5 hours

• Collection A: 15 news, 18 hours– Segmentation, named entity identification, term

extraction, retrieval

• Collection B: 15 news, 17 hours– retrieval

• 3 users– Gold annotations on the videos

Evaluation

• Segmentation– Precision: 89.94%– Recall: 86.7%– F-measure: 88.29

• Term extraction– Precision: 34.8%– Recall: 60.28%

Evaluation

• Named entity identification

Evaluation

• Retrieval– Users translate each topic to queries– 5 queries for each topic in average– Collection B

• Segmentation is based on stories

– 60% filter• Filter out results that scored less 60% in the

CIMWOS DB ranking system

Evaluation

Precision Recall F-measure

Collection A 34.75 57.75 43.39

Collection A + 60% filter 45.78 53.52 49.35

Collection B 44.78 50.24 47.36

Collection B + 60% filter 64.96 37.07 47.20