Abstract—Development of content-based image retrieval (CBIR) techniques has suffered from the lack of standardized ways for describing visual image content. Luckily, the MPEG-7, or formally “Moving Pictures Expert Group Multimedia Content Description Interface ” international standard is now emerging as both a general framework for content description and a collection of specific agreed-upon content descriptors. We have developed a neural, self-organizing technique for CBIR. Our system is named PicSOM and it is based on pictorial examples and relevance feedback (RF). The name stems from “picture ” and the self-organizing map (SOM). The PicSOM system is implemented by using tree structured SOMs. In this paper, we apply the visual content descriptors provided by MPEG-7 in the PicSOM system and compare our own image indexing technique with a reference system based on vector quantization (VQ). The results of our experiments show that the MPEG-7-defined content descriptors can be used as such in the PicSOM system even though Euclidean distance calculation, inherently used in the PicSOM system, is not optimal for all of them. Also, the results indicate that the PicSOM technique is a bit slower than the reference system in starting to find relevant images. However, when the strong RF mechanism of PicSOM begins to function, its retrieval precision exceeds that of the reference system. Index Terms—Content-based image retrieval (CBIR), MPEG-7, query by pictorial example (QBPE), relevance feedback (RF), selforganizing map (SOM), visual content description. I.

SIFT features are distinctive invariant features used to robustly describe and match digital image content between different views of a scene. While invariant to scale and rotation, and robust to other image transforms, the SIFT feature description of an image is typically large and slow to compute. This paper presents a method to reduce the size, complexity and matching time of SIFT feature sets for use in indoor image retrieval and robot localisation. Our method takes advantage of the structure of typical indoor environments to reduce the complexity of each SIFT feature and the number of SIFT features required to describe a scene.