Query specific rank fusion for image retrieval

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QUERY SPECIFIC RANK FUSION FOR IMAGE RETRIEVAL
ABSTRACT
Recently two lines of image retrieval algorithms demonstrate excellent scalability: 1)
local features indexed by a vocabularytree, and 2) holistic features indexed by compact hashing
codes. Although both of them are able to search visually similar imageseffectively, their retrieval
precision may vary dramatically among queries. Therefore, combining these two types of
methods is expectedto further enhance the retrieval precision. However, the feature
characteristics and the algorithmic procedures of these methods aredramatically different, which
is very challenging for the feature-level fusion. This motivates us to investigate how to fuse the
orderedretrieval sets, i.e., the ranks of images, given by multiple retrieval methods, to boost the
retrieval precision without sacrificing theirscalability. In this paper, we model retrieval ranks as
graphs of candidate images and propose a graph-based query specific fusionapproach, where
multiple graphs are merged and re-ranked by conducting a link analysis on a fused graph. The
retrieval quality of anindividual method is measured on-the-fly by assessing the consistency of
the top candidates’ nearest neighborhoods. Hence, it iscapable of adaptively integrating the
strengths of the retrieval methods using local or holistic features for different query images.
Thisproposed method does not need any supervision, has few parameters, and is easy to
implement. Extensive and thorough experimentshave been conducted on four public datasets,
i.e., the UK bench, Corel-5K, Holidays and the large-scale San Francisco Landmarksdatasets.
Our proposed method has achieved very competitive performance, including state-of-the-art
results on several datasets, e.g., the N-S score 3.83 for UK bench.

Image search reranking is an effective approach to refine the text-based image search result. Most existing reranking approaches are based on low-level visual features. In this paper, we propose to exploit semantic attributes for image search reranking. Based on the classifiers for all the predefined attributes, each image is represented by an attribute feature consisting of the responses from these classifiers

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