In this paper, we propose a new concept for browsing and searching in large collections of content-based indexed images. Our approach is inspired by greedy routing algorithms used in distributed networks. We define a navigation graph, called navgraph, whose vertices represent images. The edges of the navgraph are computed according to a similarity measure between indexed images. The resulting graph can be seen as an ad-hoc network of images in which a greedy routing algorithm can be applied for retrieval purposes. A request for a target image consists of a walk in the navigation graph using a greedy approach : starting from an arbitrary vertex/image, the neighbors of the current vertex are presented to the user, who iteratively selects the vertex which is the most similar to the target. We present the navgraph construction and prove its efficiency for greedy routing. We also propose a specific content-descriptor that we compare to the MPEG7 Color Layout Descriptor. Experimental results with test-users show the usability of this approach.
Data visualization techniques are penetrating
in various technological areas. In the field of
multimedia such as information search and
retrieval in multimedia archives, or digital
media production and post-production, data visualization
methodologies based on large graphs give an
exciting alternative to conventional storyboard
visualization. In this paper we develop a new
approach to visualization of multimedia (video)
documents based both on large graph clustering
and preliminary video segmenting and indexing.