|
The theory of linguistics teaches us the existence of a hierarchical structure in linguistic expressions, from letter
to word root, and on to word and sentences. By applying syntax and semantics beyond words, one can further
recognize the grammatical relationship between among words and the meaning of a sequence of words. This
layered view of a spoken language is useful for effective analysis and automated processing. Thus, it is interesting
to ask if a similar hierarchy of representation of visual information does exist. A class of techniques that have a
similar nature to the linguistic parsing is found in the Lempel-Ziv incremental parsing scheme. Based on a new
class of multidimensional incremental parsing algorithms extended from the Lempel-Ziv incremental parsing,
a new framework for image retrieval, which takes advantage of the source characterization property of the
incremental parsing algorithm, was proposed recently. With the incremental parsing technique, a given image is
decomposed into a number of patches, called a parsed representation. This representation can be thought of as
a morphological interface between elementary pixel and a higher level representation. In this work, we examine
the properties of two-dimensional parsed representation in the context of imagery information retrieval and in
contrast to vector quantization; i.e. fixed square-block representations and minimum average distortion criteria.
We implemented four image retrieval systems for the comparative study; three, called IPSILON image retrieval
systems, use parsed representation with different perceptual distortion thresholds and one uses the convectional
vector quantization for visual pattern analysis. We observe that different perceptual distortion in visual pattern
matching does not have serious effects on the retrieval precision although allowing looser perceptual thresholds
in image compression result poor reconstruction fidelity. We compare the effectiveness of the use of the parsed
representations, as constructed under the latent semantic analysis (LSA) paradigm so as to investigate their
varying capabilities in capturing semantic concepts. The result clearly demonstrates the superiority of the
parsed representation.
|
