Graph Regularised Hashing

Moran, Seán; Lavrenko, Victor

doi:10.1007/978-3-319-16354-3_15

Cited by 2 publications

(6 citation statements)

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“…In the most closely related past research authors have generally focused on either learning the hashing hyperplanes [1,4,6] or the quantisation thresholds [2,3,7,8] based on the distribution of the data. Seminal approaches for datadependent hyperplane learning either solving an eigenvalue problem to generate a set of orthogonal hyperplanes, for example using Principal Components Analysis (PCA) [9], or frame a custom objective functions that uses pairwise labels to appropriately position the hyperplanes within the feature space [1].…”

Section: Related Workmentioning

confidence: 99%

“…Seminal approaches for datadependent hyperplane learning either solving an eigenvalue problem to generate a set of orthogonal hyperplanes, for example using Principal Components Analysis (PCA) [9], or frame a custom objective functions that uses pairwise labels to appropriately position the hyperplanes within the feature space [1]. Many of these models for hyperplane learning use a single threshold placed directly at zero (for mean centered data) to quantise the projections into binary.…”

Section: Related Workmentioning

confidence: 99%

“…We couple the graph regularised projection model (GRH) of [1] and the semi-supervised quantisation model (NPQ) of [2]. The hyperplanes are learnt using GRH and the projections quantised using NPQ.…”

Section: Overview Of the Approachmentioning

confidence: 99%

“…Here T ∈ [1,3,7,15] is the number of thresholds per projected dimension and F1(t k ) is a supervised term that leverages the neighbourhood structure encoded in S. For a fixed set of thresholds…”

Section: Overview Of the Approachmentioning

confidence: 99%

“…The hashcodes exhibit the neighbourhood preserving property that similar data-points will be assigned similar (low Hamming distance) hashcodes. To compute these hashcodes, many hashing models partition the input feature space into disjoint regions with hyperplanes [1,6,9]. In the case of hyperplanes the polytope-shaped regions formed by the interPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

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Learning to Project and Binarise for Hashing Based Approximate Nearest Neighbour Search

Moran

2016

Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval

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In this paper we focus on improving the effectiveness of hashing-based approximate nearest neighbour search. Generating similarity preserving hashcodes for images has been shown to be an effective and efficient method for searching through large datasets. Hashcode generation generally involves two steps: bucketing the input feature space with a set of hyperplanes, followed by quantising the projection of the data-points onto the normal vectors to those hyperplanes. This procedure results in the makeup of the hashcodes depending on the positions of the data-points with respect to the hyperplanes in the feature space, allowing a degree of locality to be encoded into the hashcodes. In this paper we study the effect of learning both the hyperplanes and the thresholds as part of the same model. Most previous research either learn the hyperplanes assuming a fixed set of thresholds, or vice-versa. In our experiments over two standard image datasets we find statistically significant increases in retrieval effectiveness versus a host of state-ofthe-art data-dependent and independent hashing models.

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