A comparison of continuous vs. discrete image models for probabilistic image and video retrieval

The language modeling approach to retrieval is based on the philosophy that the language in a relevant document follows the same distribution as that in the query. This same philosophy can also be applied to content-based image and video retrieval, where the only difference lies in the definition of ´language´. Previous results on the TRECVID 2003 corpus have demonstrated that the visual content can be captured successfully by a continuous Gaussian mixture model. This paper investigates whether modeling the visual content by a discrete multinomial model (as used in full-text retrieval) is also viable. We compare the retrieval effectiveness obtained on the TRECVID 2003 corpus when using continuous vs. discrete keyframe models.