A bag-of-objects retrieval model for web image search

Image search reranking has been an active research topic in recent years to boost the performance of the existing web image search engine which is mostly based on textual metadata of images. Various approaches have been proposed to rerank images for general queries and argue that, they may not necessarily be optimal for queries in specific domain, e.g., object queries, since the reranking algorithms are operated on whole images, instead of the relevant parts of images. In this paper, we propose a novel bag-of-objects retrieval model for image search reranking of object queries. Firstly, we employ a common object discovery algorithm to discover query-relevant objects from the search results returned by text-based image search engine. Then, the query and its result images are represented as a language model on the query relevant object vocabulary, based on which the ranking function can be derived. As the common object discovery is unreliable and may introduce noises, we propose to incorporate the attributes of the discovered objects, e.g., size, position, etc., into the ranking function through a linear model, and the weights on the object attributes can be learned. The experiments on two subsets of Web Queries dataset comprising object queries demonstrate that our approach can significantly outperform the existing reranking methods on object queries.