A Multiple TripletRanking Model for FineGrained SketchBased Image Retrieval Finegrained sketchbased image retrieval (FGSBIR) addresses the problem of matching an input sketch with a specific photo containing the same instance. The key challenge of learning a FGSBIR model is to bridge the domain gap between photo and sketch. Most existing approaches build a joint embedding space where two domains can be directly compared. They only focus on the highly abstract features in final fully connected (FC) layer, ignore some lowlevel semantic concepts in convolutional layers. In this paper, we propose a multiple tripletranking model in FGSBIR task. Specially, we introduce an auxiliary supervision loss function in the convolutional layer, and we use the fusion of features from convolutional layer and final FC layer to build the joint embedding space. Extensive experiments show that the proposed multiple tripletranking model significantly outperforms the stateoftheart.

Sparse MultiGraph Ranking towards Social Image Retrieval xe2x80xa0 Graph ranking is one of popular and successful technique for information retrieval. However, conventional graph ranking has two shortcomings when deployed for social image search. First, as social tags are noisy and incomplete, using that, the initial ranked list of images is inaccurate and impacts the following visual reranking. Another tough issue is how to conduct querysensitive image reranking when multiple visual feature sets are available. In this work, we propose a sparse multigraph ranking framework, in which multiple graphs built on different visual features are integrated to simultaneously encode the image ranking. In particular, a sparse constraint is imposed on the fusion of different features, hoping to select a compact yet informative combination of features for different queries. To deal with the highly noisy issue inherent in social tags, a tag refinement solution along with word embedding is utilized to derive the more accurate initial ranking list, which services as the supervision signal for the proposed graph ranking framework. Extensive experimental analyses and evaluations on NUSWIDE dataset demonstrate the proposed method can achieve stateoftheart performance.