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This article presents a framework for the design of concept detection systems for image and video indexing. This framework integrates in a homogeneous way all the data and processing types. The semantic gap is crossed in a number of steps, each producing a small increase in the abstraction level of the handled data. All the data inside the semantic gap and on both sides included are seen as a homogeneous type called numcept and all the processing modules between the various numcepts are seen as a homogeneous type called operator. Concepts are extracted from the raw signal using networks of operators operating on numcepts. These networks can be represented as data-flow graphs and the introduced homogenizations allow fusing elements regardless of their nature. Low-level descriptors can be fused with intermediate of final concepts. This framework has been used to build a variety of indexing networks for images and videos and to evaluate many aspects of them. Using annotated corpora and protocols of the 2003 to 2006 TRECVID evaluation campaigns, the benefit brought by the use of individual features, the use of several modalities, the use of various fusion strategies, and the use of topologic and conceptual contexts was measured. The framework proved its efficiency for the design and evaluation of a series of network architectures while factorizing the training effort for common sub-networks.