Kernel-Based Supervised Discrete Hashing for Image Retrieval
Recently hashing has become an important tool to tackle the problem of large-scale nearest neighbor searching in computer vision. However, learning discrete hashing codes is a very challenging task due to the NP hard optimization problem. In this paper, w
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Abstract. Recently hashing has become an important tool to tackle the problem of large-scale nearest neighbor searching in computer vision. However, learning discrete hashing codes is a very challenging task due to the NP hard optimization problem. In this paper, we propose a novel yet simple kernel-based supervised discrete hashing method via an asymmetric relaxation strategy. Specifically, we present an optimization model with preserving the hashing function and the relaxed linear function simultaneously to reduce the accumulated quantization error between hashing and linear functions. Furthermore, we improve the hashing model by relaxing the hashing function into a general binary code matrix and introducing an additional regularization term. Then we solve these two optimization models via an alternative strategy, which can effectively and stably preserve the similarity of neighbors in a lowdimensional Hamming space. The proposed hashing method can produce informative short binary codes that require less storage volume and lower optimization time cost. Extensive experiments on multiple benchmark databases demonstrate the effectiveness of the proposed hashing method with short binary codes and its superior performance over the state of the arts.
Keywords: Supervised kernel hashing lated quantization error reduction
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· Discrete constraint · Accumu-
Introduction
Over the past decade, hashing has attracted considerable attentions in computer vision [1–4] and machine learning [5,6] communities. With the increasing of visual data including images and videos, it is favorable to apply compact hashing codes to data storing and content searching. Basically hashing encodes each high-dimensional data into a set of binary codes and meanwhile preserves the similarity between neighbors. Recent literature reports that when each image is encoded into several tens of binary bits, the storage of one hundred million images requires only less than 1.5 GB [7] and searching in a collection of millions of images costs a constant time [8,9]. c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part VII, LNCS 9911, pp. 419–433, 2016. DOI: 10.1007/978-3-319-46478-7 26
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Nowadays many hashing methods have been proposed. Based on whether semantic information is considered, they can be grouped into two major categories: unsupervised and supervised. Unsupervised hashing methods aim to explore the intrinsic structure of data to preserve the similarity of neighbors without any supervision. Local sensitive hashing (LSH) [10] is one of the most popular unsupervised hashing approaches and has been applied to tackling many large-scale data problems. However, LSH is a data-independent method that uses random projection to generate binary codes, thereby requiring long binary codes to achieve satisfactory retrieval accuracy. On the other hand, many datadependent methods, such as spectral hashing (SH) [6], anchor graph hashing (AGH) [11] and iterative quantization (ITQ) [12], have been proposed to learn
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