Authors:
Leonardo Joao
1
;
2
and
Alexandre Falcao
1
Affiliations:
1
Institute of Computing, State University of Campinas, Campinas, 13083-872, São Paulo, Brazil
;
2
LIGM, Univ. Gustave-Eiffel, Marne-la-Valée, F-77454, France
Keyword(s):
Salient Object Detection, Saliency Enhancement, Deep-Learning, Superpixel-Based Saliency, Iterative Saliency.
Abstract:
Saliency Object Detection (SOD) has several applications in image analysis. The methods have evolved from image-intrinsic to object-inspired (deep-learning-based) models. However, when a model fails, there is no alternative to enhance its saliency map. We fill this gap by introducing a hybrid approach, the Iterative Saliency Enhancement over Superpixel Similarity (ISESS), that iteratively generates enhanced saliency maps by executing two operations alternately: object-based superpixel segmentation and superpixel-based saliency estimation - cycling operations never exploited. ISESS estimates seeds for superpixel delineation from a given saliency map and defines superpixel queries in the foreground and background. A new saliency map results from color similarities between queries and superpixels at each iteration. The process repeats, and, after a given number of iterations, the generated saliency maps are combined into one by cellular automata. Finally, the resulting map is merged wit
h the initial one by the maximum between their average values per superpixel. We demonstrate that our hybrid model consistently outperforms three state-of-the-art deep-learning-based methods on five image datasets.
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