Abstract
The potential value of hashing techniques has led to it becoming one of the most active research areas in computer vision and multimedia. However, most existing hashing methods for image search and retrieval are based on global feature representations, which are susceptible to image variations such as viewpoint changes and background cluttering. Traditional global representations gather local features directly to output a single vector without the analysis of the intrinsic geometric property of local features. In this paper, we propose a novel unsupervised hashing method called unsupervised bilinear local hashing (UBLH) for projecting local feature descriptors from a high-dimensional feature space to a lower-dimensional Hamming space via compact bilinear projections rather than a single large projection matrix. UBLH takes the matrix expression of local features as input and preserves the feature-to-feature and image-to-image structures of local features simultaneously. Experimental results on challenging data sets including Caltech-256, SUN397, and Flickr 1M demonstrate the superiority of UBLH compared with state-of-the-art hashing methods.
Highlights
L EARNING to hash has received substantial attention due to its potential in various applications such as data mining, pattern recognition, and information retrieval [1]–[7]
Due to above reasons, hashing techniques are proposed to effectively embed data from a high-dimensional feature space into a similaritypreserved low-dimensional Hamming space where an approximate nearest neighbor (ANN) of a given query can be found with sublinear time complexity
This paper aims at unsupervised linear hashing for local features, which makes unsupervised bilinear local hashing (UBLH) effective and practical for real-world applications without class label information
Summary
L EARNING to hash has received substantial attention due to its potential in various applications such as data mining, pattern recognition, and information retrieval [1]–[7]. For realistic visual retrieval tasks, these global hashing techniques cannot cope with different complications appearing in the images such as cluttering, scaling, occlusion, and change of lighting conditions These aspects are more invariant in local featuresbased representations such as bag-of-features [9], [10], since such representations are statistical distributions of image patches and tend to be more robust in challenging and noisy scenarios. Good at extracting the global intensity, color, texture, and gradient information of images, but will ignore the detailed information in the query image without analyzing the intrinsic geometric property of local features This problem may heavily limit the effectiveness on applications that demand more accurate retrieval results for complex scene/object images. UBLH simultaneously preserves the F2F and I2I structures which can be regarded as the local and global structures respectively in the original feature space
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