ISSN : 1796-2048
Volume : 4    Issue : 5    Date : October 2009

Bridging the Semantic Gap for Texture-based Image Retrieval and Navigation
Najlae Idrissi, José Martinez, and Driss Aboutajdine
Page(s): 277-283
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In this study, we propose a new semantic approach for interpreting textures in natural terms. In our
system, the user can reach desired textures by navigating into a hierarchy of sub collections
previously held (offline). The originality of the proposed approach stems from two reasons: (1)- the
intrinsic properties of the texture features extracted from the co-occurrence matrices have never
been used before and (2)- it provides some degree of tolerance to generate the classes semantic
which is not available with the standard unsupervised clustering algorithms such as kmeans. Thus,
our contibutions in this study are threefold. (1)- Our approach maps low-level visual statistical
features to high-level semantic concepts; it bridges the gap between the two levels enabling to
retrieve and browse image collections by their high-level semantic concepts. (2)- Our system
models the human perception subjectivity with the degree of tolerance and (3)- it provides an easy
interface for navigating and browsing image collections to reach target collections. A comparative
study with the unsupervised clustering algorithm k-means reveals the effectiveness of the proposed

Index Terms
Texture features; Image retrieval; Semantic gap; Navigation; Co-occurrence matrices