  The widespread image applications have greatly promoted the vision-based
tasks, in which the Image Quality Assessment (IQA) technique has become an
increasingly significant issue. For user enjoyment in multimedia systems, the
IQA exploits image fidelity and aesthetics to characterize user experience;
while for other tasks such as popular object recognition, there exists a low
correlation between utilities and perceptions. In such cases, the
fidelity-based and aesthetics-based IQA methods cannot be directly applied. To
address this issue, this paper proposes a utility-oriented IQA in object
recognition. In particular, we initialize our research in the scenario of
underwater fish detection, which is a critical task that has not yet been
perfectly addressed. Based on this task, we build an Underwater Image Utility
Database (UIUD) and a learning-based Underwater Image Utility Measure (UIUM).
Inspired by the top-down design of fidelity-based IQA, we exploit the deep
models of object recognition and transfer their features to our UIUM.
Experiments validate that the proposed transfer-learning-based UIUM achieves
promising performance in the recognition task. We envision our research
provides insights to bridge the researches of IQA and computer vision.
