Directly operable image representation of multiscale primal sketch
In this paper, we propose a versatile semantics-driven image representation that can support many common operations in visual computing and communications, in addition to being itself an efficient image coding scheme. The proposed image representation is based on a semantically meaningful construct called multiscale primal sketch (NIPS). The NIPS consists of edges that are extracted and organized successively from fine to coarse scales. The edges are further classified into two types: pulse edge and step edge. NIPS is an intermediate-level image representation, which is between pixel-based (low-level) and object-based (high-level) descriptions. The NIPS image representation reaches a good compromise between its construction cost and descriptive power. It has a compact form, and hence is amenable to image compression. Furthermore, because the new representation consists of semantically meaningful primitives-edges of different scales and types, and background-many common image operations, such as classification, restoration, detection, and content-based retrieval, can be performed directly in the NIPS framework, without first converting the coded image back to the spatial domain.
image representation image compression content-based retrieval edge-based scene analysis and classification multiresolution analysis