Feature Combination in Kernel Space for Distance Based Image Hashing

Hassan, Ehtesham; Chaudhury, Santanu; Gopal, M.

doi:10.1109/tmm.2012.2190388

Cited by 21 publications

(9 citation statements)

References 53 publications

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“…Liu et al [4] proposed kernel based supervised hashing formulation based on code inner products which employs greedy gradient descent algorithm for solving the hash functions. In [5], Kernel distance based hashing (KDBH) is proposed which presents novel multiple kernel learning (MKL) formulation for indexing by learning the optimum kernel space by parameterized linear combination of base kernels. The MKL objective is learned in Genetic Algorithm (GA) based framework by optimizing over search accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we present novel MKL for KDBH which learns the optimum kernel space for hashing jointly optimizing over accuracy and time unlike [5] where the objective was only to optimize upon the search accuracy. Our notion of accuracy inherently accounts for the ranking of retrieved objects which is the distinguishing parameter in solving a retrieval and recognition problem by ANN search.…”

Section: Related Workmentioning

confidence: 99%

“…The KDBH discussed in [5] applies the kernel space representation of line-projection function as follows.…”

Section: Optimization Problem Formulationmentioning

confidence: 99%

“…The indexing framework follows the conventional hashing based indexing framework. Refer [5] for further details related to KDBH. The MKL defined in [5], learns the kernel K for hashing from the base kernel set provided from the user.…”

Section: Optimization Problem Formulationmentioning

confidence: 99%

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Design of Multi-kernel Distance Based Hashing with Multiple Objectives for Image Indexing

Gaur

Hassan

Chaudhury

2014

2014 22nd International Conference on Pattern Recognition

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Approximate nearest neighbor (ANN) search provides computationally viable option for retrieval from large document collection. Hashing based techniques are widely regarded as most efficient methods for ANN based retrieval. It has been established that by combination of multiple features in a multiple kernel learning setup can significantly improve the effectiveness of hash codes. The paper presents a novel image indexing method based on multiple kernel learning, which combines multiple features by combinatorial optimization of time and search complexity. The framework is built upon distance based hashing, where the existing kernel distance based hashing formulation adopts linear combination of kernels in tune with optimum search accuracy. In this direction, a novel multiobjective formulation for optimizing the search time as well as accuracy is proposed which is subsequently solved in Genetic algorithm based solution framework for obtaining the pareto-optimal solutions. We have performed extensive experimental evaluation of proposed concepts on different datasets showing improvement in comparison with the existing methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The KDBH discussed in [5] applies the kernel space representation of line-projection function as follows.…”

Section: Optimization Problem Formulationmentioning

confidence: 99%

Section: Optimization Problem Formulationmentioning

confidence: 99%

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Design of Multi-kernel Distance Based Hashing with Multiple Objectives for Image Indexing

Gaur

Hassan

Chaudhury

2014

2014 22nd International Conference on Pattern Recognition

Self Cite

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“…Attempt to overcome these challenge has led to an emerging multimedia technology known as image hashing. It is a technique that extracts a short sequence from the image to represent its contents, which is used for various image processing applications (Ahmed et al 2010; Hassan et al 2012; Lu et al 2004; Lu and Wu 2010; Lv and Wang 2009; Qin et al 2012; Slaney and Casey 2008; Tang et al 2008). …”

Section: Introductionmentioning

confidence: 99%

Robust image hashing using ring partition-PGNMF and local features

2016

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BackgroundImage authentication is one of the challenging research areas in the multimedia technology due to the availability of image editing tools. Image hash may be used for image authentication which should be invariant to perceptually similar image and sensitive to content changes. The challenging issue in image hashing is to design a system which simultaneously provides rotation robustness, desirable discrimination, sensitivity and localization of forged area with minimum hash length.MethodsIn this paper, a perceptually robust image hashing technique based on global and local features has been proposed. The Global feature was extracted using ring partition and projected gradient nonnegative matrix factorization (PGNMF). The ring partitioning technique converts a square image into a secondary image that makes the system rotation invariant. The PGNMF which is usually faster than the other NMFs has been used to reduce the dimension of the secondary image to generate the shorter hash sequence. The local features extracted from the salient regions of the image help to localize the forged region in the maliciously manipulated images. The image hashing techniques that use only global features are limited in discrimination.ResultsThe experimental results reveal that the proposed image hashing method based on global and local features provides better discrimination capability. The proposed hashing method is tested on large image sets collected from the different standard database. It is observed from the experimental results that the proposed system is robust to content-preserving operations and is capable of localizing the counterfeit area.ConclusionsThe combination of global and local features is robust against the content-preserving operations, which has a desirable discriminative capability. The proposed system may be used in image authentication, forensic evidence, and image retrieval, etc.

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Info-Graphics Retrieval: A Multi-kernel Distance Based Hashing Scheme

Garg

Chaudhury

2017

Computer Vision, Graphics, and Image Processing

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Feature Combination in Kernel Space for Distance Based Image Hashing

Cited by 21 publications

References 53 publications

Design of Multi-kernel Distance Based Hashing with Multiple Objectives for Image Indexing

Design of Multi-kernel Distance Based Hashing with Multiple Objectives for Image Indexing

Robust image hashing using ring partition-PGNMF and local features

Info-Graphics Retrieval: A Multi-kernel Distance Based Hashing Scheme

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