The use of video and audio features for automated annotation of audio-visual data is becoming widespread. A major limitation of many of the current methods is that the stored indexing features are too low-level-they relate directly to properties of the data. We apply a further stage of processing that associates the feature measurements with real-world objects or events. The outputs, which we call "cues", denote the probability of the object being present in the scene. An additional advantage of this approach is that the cues from different types of features are presented in a homogeneous way.

The multimodal neighbourhood signature (MNS) method has given acceptable results both for the colour-based image retrieval and the object recognition task. Local colour content is concisely represented by invariant features computed from neighbourhoods with multimodal colour density function. In this paper, efficiency related issues regarding the MNS algorithm are investigated. Its performance, speed, sensitivity to internal parameters and storage requirements are tested on a standard colour object recognition experiment. Very good recognition rate (99.9%) was achieved in real time. The MNS signature size is a few hundred bytes on average, an important property for retrieval from large databases. The algorithmic complexity of signature computation and matching are analysed and efficient implementations are proposed.