Multiple Query ContentBased Image Retrieval Using Relevance Feature Weight Learning We propose a novel multiple query retrieval approach, named weightlearner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual featuredescriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the userxe2x80x99s highlevel semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.

Deep Multilabel Hashing for Image Retrieval Due to its low storage cost and fast query speed, hashing has been widely applied to approximate nearest neighbor search for largescale image retrieval, while deep hashing further improves the retrieval quality by learning a good image representation. However, existing deep hash methods simplify multilabel images into singlelabel processing, so the rich semantic information from multilabel is ignored. Meanwhile, the imbalance of similarity information leads to the wrong sample weight in the loss function, which makes unsatisfactory training performance and lower recall rate. In this paper, we propose Deep MultiLabel Hashing (DMLH) model that generates binary hash codes which retain the semantic relationship of multilabel of the image. The contributions of this new model mainly include the following two aspects: (1) A novel sample weight calculation model adaptively adjusts the weight of the sample pair by calculating the semantic similarity of the multilabel image pairs. (2) The sample weight crossentropy loss function, which is designed according to the similarity of the image, adjusts the balance of similar image pairs and dissimilar image pairs. Extensive experiments demonstrate that the proposed method can generate hash codes which achieve better retrieval performance on two benchmark datasets, NUSWIDE and MSCOCO.