With the rapid growth of web images, hashing has received increasing interests in large scale image retrieval. Research efforts have been devoted to learning compact binary codes that preserve semantic similarity based on labels. However, most of these hashing methods are designed to handle simple binary similarity. The complex multilevel semantic structure of images associated with multiple labels have not yet been well explored. Here we propose a deep semantic ranking based method for learning hash functions that preserve multilevel semantic similarity between multi-label images. In our approach, deep convolutional neural network is incorporated into hash functions to jointly learn feature representations and mappings from them to hash codes, which avoids the limitation of semantic representation power of hand-crafted features. Meanwhile, a ranking list that encodes the multilevel similarity information is employed to guide the learning of such deep hash functions. An effective scheme based on surrogate loss is used to solve the intractable optimization problem of nonsmooth and multivariate ranking measures involved in the learning procedure. Experimental results show the superiority of our proposed approach over several state-of-the-art hashing methods in term of ranking evaluation metrics when tested on multi-label image datasets.
Furthermore, order-preserving approaches, which are more related to this paper, explicitly use ranking information in objective functions to learn hash codes that preserve the similarity order in the feature or semantic space. Order preserving hashing (OPH) @cite_13 formulates an alignment between the similarity orders computed respectively from the original Euclidean space and the Hamming space, which can be solved using the quadratic penalty algorithm. On the basis of @cite_4 , hamming distance metric learning (HDML) @cite_7 develops a metric learning framework based on a triplet ranking loss to preserve relative similarity. However, this triplet loss function only considers local ranking information and is limited in capturing information about multilevel similarity. By using a triplet representation for listwise supervision, ranking-based supervised hashing (RSH) @cite_23 minimizes the inconsistency of ranking order between the hamming and original spaces to keep global ranking order. Different from RSH, our method leverages deep learning model to discover deeper semantic similarity and can scale well on large training sets. Column generation hashing (CGH) @cite_22 and StructHash @cite_17 combine ranking information with the boosting framework to learn a weighted hamming embedding. In contrast, our method needs no extra weight to rank hash codes.
@cite_4 @cite_22 @cite_7 @cite_23 @cite_13 @cite_17
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In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. We make use of a triplet loss because this has been shown to be most effective for ranking problems. However, training in previous works can be prohibitively expensive due to the fact that optimization is directly performed on the triplet space, where the number of possible triplets for training is cubic in the number of training examples. To address this issue, we propose to formulate high-order binary codes learning as a multi-label classification problem by explicitly separating learning into two interleaved stages. To solve the first stage, we design a large-scale high-order binary codes inference algorithm to reduce the high-order objective to a standard binary quadratic problem such that graph cuts can be used to efficiently infer the binary code which serve as the label of each training datum. In the second stage we propose to map the original image to compact binary codes via carefully designed deep convolutional neural networks (CNNs) and the hashing function fitting can be solved by training binary CNN classifiers. An incremental interleaved optimization strategy is proffered to ensure that these two steps are interactive with each other during training for better accuracy. We conduct experiments on several benchmark datasets, which demonstrate both improved training time (by as much as two orders of magnitude) as well as producing state-of-the-art hashing for various retrieval tasks. In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. We make use of a triplet loss because this has been shown to be most effective for ranking problems. However, training in previous works can be prohibitively expensive due to the fact that optimization is directly performed on the triplet space, where the number of possible triplets for training is cubic in the number of training examples. To address this issue, we propose to formulate high-order binary codes learning as a multi-label classification problem by explicitly separating learning into two interleaved stages. To solve the first stage, we design a large-scale high-order binary codes inference algorithm to reduce the high-order objective to a standard binary quadratic problem such that graph cuts can be used to efficiently infer the binary codes which serve as the labels of each training datum. In the second stage we propose to map the original image to compact binary codes via carefully designed deep convolutional neural networks (CNNs) and the hashing function fitting can be solved by training binary CNN classifiers. An incremental interleaved optimization strategy is proffered to ensure that these two steps are interactive with each other during training for better accuracy. We conduct experiments on several benchmark datasets, which demonstrate both improved training time (by as much as two orders of magnitude) as well as producing state-of-the-art hashing for various retrieval tasks. Hashing has gained considerable attention on large-scale similarity search, due to its enjoyable efficiency and low storage cost. In this paper, we study the problem of learning hash functions in the context of multi-modal data for cross-modal similarity search. Notwithstanding the progress achieved by existing methods, they essentially learn only one common hamming space, where data objects from all modalities are mapped to conduct similarity search. However, such method is unable to well characterize the flexible and discriminative local (neighborhood) structure in all modalities simultaneously, hindering them to achieve better performance. Bearing such stand-out limitation, we propose to learn heterogeneous hamming spaces with each preserving the local structure of data objects from an individual modality. Then, a novel method to learning bridging mapping for cross-modal hashing, named LBMCH, is proposed to characterize the cross-modal semantic correspondence by seamlessly connecting these distinct hamming spaces. Meanwhile, the local structure of each data object in a modality is preserved by constructing an anchor based representation, enabling LBMCH to characterize a linear complexity w.r.t the size of training set. The efficacy of LBMCH is experimentally validated against real-world cross-modal datasets. Hashing techniques have been intensively investigated for large scale vision applications. Recent research has shown that leveraging supervised information can lead to high quality hashing. However, most existing supervised hashing methods only construct similarity-preserving hash codes. Observing that semantic structures carry complementary information, we propose the idea of cotraining for hashing, by jointly learning projections from image representations to hash codes and classification. Specifically, a novel deep semantic-preserving and ranking-based hashing (DSRH) architecture is presented, which consists of three components: a deep CNN for learning image representations, a hash stream of a binary mapping layer by evenly dividing the learnt representations into multiple bags and encoding each bag into one hash bit, and a classification stream. Mean-while, our model is learnt under two constraints at the top loss layer of hash stream: a triplet ranking loss and orthogonality constraint. The former aims to preserve the relative similarity ordering in the triplets, while the latter makes different hash bit as independent as possible. We have conducted experiments on CIFAR-10 and NUS-WIDE image benchmarks, demonstrating that our approach can provide superior image search accuracy than other state-of-the-art hashing techniques.