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Visual phrase considers multiple visual words and captures extra spatial clues among them. Thus, visual phrase shows better discriminative power than single visual word in image retrieval and matching. Not withstanding their success, existing visual phrases still show obvious shortcomings: (1) limited flexibility, i.e., visual phrases are considered for matching only if they contain the same number of visual words; (2) large quantization error and low repeatability, i.e., quantization errors in visual words are aggregated in visual word combinations and visual phrases, making them harder to be matched than single visual words. To avoid these issues, we propose multi-order visual phrase (MVP) which contains two complementary clues: center visual word quantized from the local descriptor of each image keypoint and the visual and spatial clues of multiple nearby keypoints. Two MVPs are flexibly matched by first matching their center visual words, then estimating a match confidence by checking the spatial and visual consistency of their neighbor keypoints. Therefore, center visual word matching equals to traditional visual word matching, but the neighbor spatial and visual clues checking significantly boosts the discriminative power. MVP does not scarify the repeatability of single visual word and is more robust to quantization error than existing visual phrases. We test our approach in three image retrieval tasks on UKbench, Oxford5K, and 1 million distractor images collected from Flickr. Comparisons with recent retrieval approaches and existing visual phrase features clearly demonstrate the competitive accuracy and significantly better efficiency of MVP. [ABSTRACT FROM AUTHOR]

Online image hashing has attracted increasing research attention recently, which receives large-scale data in a streaming manner to update the hash functions on-the-fly. Its key challenge lies in the difficulty of balancing the learning timeliness and model accuracy. To this end, most works follow a supervised setting, i.e., using class labels to boost the hashing performance, which defects in two aspects: first, strong constraints, e.g., orthogonal or similarity preserving, are used, which however are typically relaxed and lead to large accuracy drops. Second, large amounts of training batches are required to learn the up-to-date hash functions, which largely increase the learning complexity. To handle the above challenges, a novel supervised online hashing scheme termed Hadamard Matrix Guided Online Hashing (HMOH) is proposed in this paper. Our key innovation lies in introducing Hadamard matrix, which is an orthogonal binary matrix built via Sylvester method. In particular, to release the need of strong constraints, we regard each column of Hadamard matrix as the target code for each class label, which by nature satisfies several desired properties of hashing codes. To accelerate the online training, LSH is first adopted to align the lengths of target code and to-be-learned binary code. We then treat the learning of hash functions as a set of binary classification problems to fit the assigned target code. Finally, extensive experiments on four widely-used benchmarks demonstrate the superior accuracy and efficiency of HMOH over various state-of-the-art methods. Codes can be available at https://github.com/lmbxmu/mycode. [ABSTRACT FROM AUTHOR]

Recently, the bag-of-words representation is widely applied in the image retrieval applications. In this model, visual word is a core component. However, compared with text retrieval, one major problem associated with image retrieval consists in the visual word ambiguity, i.e., a trade-off between precision and recall of visual matching. To address this problem, this paper proposes a tensor index structure to improve precision and recall simultaneously. Essentially, the tensor index is a multi-dimensional index structure. It combines the strengths of two state-of-the-art indexing strategies, i.e., the inverted multi-index [Babenko and Lempitsky (Computer vision and pattern recognition (CVPR), 2012 IEEE Conference, 3069-3076, )] as well as the joint inverted index [Xia et al. (ICCV, )] which are initially designed for approximate nearest neighbor search problems. This paper, instead, exploits their usage in the scenario of image retrieval and provides insights into how to combine them effectively. We show that on the one hand, the multi-index enhances the discriminative power of visual words, thus improving precision; on the other hand, the introduction of multiple codebooks corrects quantization artifacts, thus improving recall. Extensive experiments on two benchmark datasets demonstrate that tensor index significantly improves the baseline approach. Moreover, when incorporating methods such as Hamming embedding, we achieve competitive performances compared to the state-of-the-art ones. [ABSTRACT FROM AUTHOR]