We investigate the challenging issue of joint audio-visual analysis of generic videos targeting at semantic concept detection. We propose to extract a novel representation, the Short-term Audio-Visual Atom (S-AVA), for improved concept detection. An S-AVA is defined as a short-term region track associated with regional visual features and background audio features. An effective algorithm, named ShortTerm Region tracking with joint Point Tracking and Region Segmentation (STR-PTRS), is developed to extract S-AVAs from generic videos under challenging conditions such as uneven lighting, clutter, occlusions, and complicated motions of both objects and camera. Discriminative audio-visual codebooks are constructed on top of S-AVAs using Multiple Instance Learning. Codebook-based features are generated for semantic concept detection. We extensively evaluate our algorithm over Kodak's consumer benchmark video set from real users. Experimental results confirm significant performance improvements -over 120% MAP gain compared to alternative approaches using static region segmentation without temporal tracking. The joint audio-visual features also outperform visual features alone by an average of 8.5% (in terms of AP) over 21 concepts, with many concepts achieving more than 20%.  Categories and Subject Descriptors General Terms Algorithms, Experimentation Semantic concept detection, joint audio-visual analysis, shortterm audio-visual atom, audio-visual codebook Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. MM '09, October 19-24, 2009, Beijing, China. Copyright 2009 track of a birthday cake and the background birthday music form a salient audio-visual cue to describe "birthday" videos. The short-term region track of a horse and the background horse running footstep sound give a salient audio-visual cue for the "animal" concept.
REF proposes a method to extract a novel representation, the Short-term AudioVisual Atom (S-AVA), for improved semantic concept detection in videos.
Short-term audio-visual atoms for generic video concept classification
MM '09
Computer Science