Bridging the Semantic Gap for Texture-based Image Retrieval and Navigation
Najlae Idrissi, José M. Martínez, Driss Aboutajdine · Journal of Multimedia · 2009
Abstract — 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 k-means. 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 approach.