  As a natural way for human-computer interaction, fixation provides a
promising solution for interactive image segmentation. In this paper, we focus
on Personal Fixations-based Object Segmentation (PFOS) to address issues in
previous studies, such as the lack of appropriate dataset and the ambiguity in
fixations-based interaction. In particular, we first construct a new PFOS
dataset by carefully collecting pixel-level binary annotation data over an
existing fixation prediction dataset, such dataset is expected to greatly
facilitate the study along the line. Then, considering characteristics of
personal fixations, we propose a novel network based on Object Localization and
Boundary Preservation (OLBP) to segment the gazed objects. Specifically, the
OLBP network utilizes an Object Localization Module (OLM) to analyze personal
fixations and locates the gazed objects based on the interpretation. Then, a
Boundary Preservation Module (BPM) is designed to introduce additional boundary
information to guard the completeness of the gazed objects. Moreover, OLBP is
organized in the mixed bottom-up and top-down manner with multiple types of
deep supervision. Extensive experiments on the constructed PFOS dataset show
the superiority of the proposed OLBP network over 17 state-of-the-art methods,
and demonstrate the effectiveness of the proposed OLM and BPM components. The
constructed PFOS dataset and the proposed OLBP network are available at
https://github.com/MathLee/OLBPNet4PFOS.
