Impact of novel sources on content-based image and video retrieval

The problem of content-based image and video retrieval with textual queries is often posed as that of visual concept classification, where classifiers for a set of predetermined visual concepts are trained using a set of manually annotated images. Such a formulation implicitly assumes that the training data has similar distributional characteristics as that of the data which need to be indexed. In this paper we demonstrate empirically that even within the relatively narrow domain of news videos collected from a variety of news programs and broadcasters, the assumption of distributional similarity of visual features does not hold across programs from different broadcasters. This is manifested in considerable degradation of ranked retrieval performance on novel sources. We observe that concepts whose spatial locations remain relatively fixed between various sources are also more robust to source mismatches, and vice versa. We also show that a simple averaging of multiple visual detec...

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Arnab Ghoshal, Sanjeev Khudanpur, Dietrich Klakow
• Keywords: ICASSP 2009, Signal Processing, Similar Distributional Characteristics, Visual Concept Classification, Visual Concepts