Query Adaptive Late Fusion for Image Retrieval
release_3rvbbzxgg5cvbmmvz4ar3jlsmq
by
Zhongdao Wang, Liang Zheng, Shengjin Wang
2018
Abstract
Feature fusion is a commonly used strategy in image retrieval tasks, which
aggregates the matching responses of multiple visual features. Feasible sets of
features can be either descriptors (SIFT, HSV) for an entire image or the same
descriptor for different local parts (face, body). Ideally, the to-be-fused
heterogeneous features are pre-assumed to be discriminative and complementary
to each other. However, the effectiveness of different features varies
dramatically according to different queries. That is to say, for some queries,
a feature may be neither discriminative nor complementary to existing ones,
while for other queries, the feature suffices. As a result, it is important to
estimate the effectiveness of features in a query-adaptive manner. To this end,
this article proposes a new late fusion scheme at the score level. We base our
method on the observation that the sorted score curves contain patterns that
describe their effectiveness. For example, an "L"-shaped curve indicates that
the feature is discriminative while a gradually descending curve suggests a bad
feature. As such, this paper introduces a query-adaptive late fusion pipeline.
In the hand-crafted version, it can be an unsupervised approach to tasks like
particular object retrieval. In the learning version, it can also be applied to
supervised tasks like person recognition and pedestrian retrieval, based on a
trainable neural module. Extensive experiments are conducted on two object
retrieval datasets and one person recognition dataset. We show that our method
is able to highlight the good features and suppress the bad ones, is resilient
to distractor features, and achieves very competitive retrieval accuracy
compared with the state of the art. In an additional person re-identification
dataset, the application scope and limitation of the proposed method are
studied.
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