Novelty detection for cross-lingual news stories with visual duplicates and speech transcripts

• Main Authors: WU, Xiao, HAUPTMANN, Alexander G., NGO, Chong-wah
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2007
• Subjects: Cross-lingual information retrieval; Language model; Multimodality; Near-duplicate keyframes; News videos; Novelty and redundancy detection; Similarity measure; Programming Languages and Compilers; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/6380; https://ink.library.smu.edu.sg/context/sis_research/article/7383/viewcontent/1291233.1291274.pdf
• Institution: Singapore Management University

An overwhelming volume of news videos from different channels and languages is available today, which demands automatic management of this abundant information. To effectively search, retrieve, browse and track cross-lingual news stories, a news story similarity measure plays a critical role in assessing the novelty and redundancy among them. In this paper, we explore the novelty and redundancy detection with visual duplicates and speech transcripts for cross-lingual news stories. News stories are represented by a sequence of keyframes in the visual track and a set of words extracted from speech transcript in the audio track. A major difference to pure text documents is that the number of keyframes in one story is relatively small compared to the number of words and there exist a large number of non-near-duplicate keyframes. These features make the behavior of similarity measures different compared to traditional textual collections. Furthermore, the textual features and visual features complement each other for news stories. They can be further combined to boost the performance. Experiments on the TRECVID-2005 cross-lingual news video corpus show that approaches on textual features and visual features demonstrate different performance, and measures on visual features are quite effective. Overall, the cosine distance on keyframes is still a robust measure. Language models built on visual features demonstrate promising performance. The fusion of textual and visual features improves overall performance.