An efficient feature descriptor based on synthetic basis functions and uniqueness matching strategy

Desai, Alok; Lee, Dah-Jye; Ventura, Dan

doi:10.1016/j.cviu.2015.09.005

Cited by 15 publications

(45 citation statements)

References 36 publications

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“…As shown in Figure 6, Bob is able to search a database of 10,000 within a period of 162.3 ms using SEPIM. SEPIM induces a higher search cost of just 14% over schemes which search images using plaintext [21,22,24,25]. The additional time cost of our work can be considered as a reasonable cost for achieving a secure matching.…”

Section: Search Timementioning

confidence: 91%

“…Figure 8 evaluates the level of search precision to which SEPIM conforms under variable dimension reductions. This formula employs MAP, which was introduced in Section 3.2, and demonstrates SEPIM acquiring a similar precision in search as plaintext image search schemes [21,22,24,25], with just 2% lower precision. SURF shows better performances as compared with SYBA and BRIEF approaches.…”

Section: Search Timementioning

confidence: 99%

“…In recent years, researchers have proposed feature descriptors based on intensity such as binary robust independent elementary features (BRIEF) [24] and SYnthetic BAsis (SYBA) [25]. The BRIEF and SYBA descriptors are binary descriptors that consist of a binary string including the results of intensity comparison at random pre-determined pixel locations.…”

Section: Related Workmentioning

confidence: 99%

“…[24] SYBA Ref. [25] Search Cost (ms) Dimension Reduction Clearly, PCA transform results in some data loss in relation to specific image features, possibly impacting the search precision in a limited way and somewhat offsetting the self-evident relationship between improved system effectiveness and lowering the descriptor vector using PCA transform. In the next part, we will analyse the search precision of SEPIM, and evaluate how to establish correct PCA strength might help to balance the effectiveness of a SEPIM-influenced system with regard to its efficiency and precision.…”

Section: Search Timementioning

confidence: 99%

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SEPIM: Secure and Efficient Private Image Matching

et al. 2016

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Matching a particular image within extensive datasets has become increasingly pressing in many practical fields. Hence, a number of matching methods have been developed when confidential images are used in image matching between a pair of security agencies, but they are limited by either search cost or search precision. In this paper, we propose a privacy-preserving private image matching scheme between two parties where images are confidential, namely secure and efficient private image matching (SEPIM). The descriptor set of the queried party needs to be generated and encrypted properly with the use of a secret key at the queried party side before being transferred to the other party. We present the development and validation of a secure scheme to measure the cosine similarity between two descriptor sets. To hasten the search process, we construct a tree-based index structure by utilizing the k-means clustering algorithm. The method can work without using any image encryption, sharing, and trusted third party. SEPIM is relatively efficient when set against other methods of searching images over plaintexts, and shows a higher search cost of just 14% and reduction in search precision of just 2%. We conducted several empirical analyses on real image collections to demonstrate the performance of our work.

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Section: Search Timementioning

confidence: 91%

Section: Search Timementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Search Timementioning

confidence: 99%

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SEPIM: Secure and Efficient Private Image Matching

et al. 2016

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“…These binary descriptors algorithms are sensitive to image variations and can have fewer accurate matches and a reduced match count compared to SIFT and SURF. A more detailed comparison of these algorithms is included in the next section.In our previous work, a new binary descriptor algorithm called SYnthetic BAsis (SYBA) descriptor was developed to obtain a higher percentage of correct matches [8]. SYBA performs binary comparisons between the binarized feature region and synthetic basis images to obtain a compact feature descriptor.…”

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confidence: 99%

Hardware Friendly Robust Synthetic Basis Feature Descriptor

et al. 2019

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Finding corresponding image features between two images is often the first step for many computer vision algorithms. This paper introduces an improved synthetic basis feature descriptor algorithm that describes and compares image features in an efficient and discrete manner with rotation and scale invariance. It works by performing a number of similarity tests between the feature region surrounding the feature point and a predetermined number of synthetic basis images to generate a feature descriptor that uniquely describes the feature region. Features in two images are matched by comparing their descriptors. By only storing the similarity of the feature region to each synthetic basis image, the overall storage size is greatly reduced. In short, this new binary feature descriptor is designed to provide high feature matching accuracy with computational simplicity, relatively low resource usage, and a hardware friendly design for real-time vision applications. Experimental results show that our algorithm produces higher precision rates and larger number of correct matches than the original version and other mainstream algorithms and is a good alternative for common computer vision applications. Two applications that often have to cope with scaling and rotation variations are included in this work to demonstrate its performance. they perform well on intensity images and are robust against image variations, these algorithms require complex computations and large memory storage. These requirements are not desirable for resource-limited embedded applications and applications that require real-time performance.Several binary feature description algorithms have been developed to use simpler computation to provide descriptor with smaller size. Binary Robust Independent Elementary Features (BRIEF) [5] and the Binary Robust Invariant Scalable Key-points (BRISK) [6] are two representative and popular algorithms. A newer and improved version of BRIEF called ORB or rBRIEF has been developed [7] to cope with rotation invariance. These binary descriptors algorithms are sensitive to image variations and can have fewer accurate matches and a reduced match count compared to SIFT and SURF. A more detailed comparison of these algorithms is included in the next section.In our previous work, a new binary descriptor algorithm called SYnthetic BAsis (SYBA) descriptor was developed to obtain a higher percentage of correct matches [8]. SYBA performs binary comparisons between the binarized feature region and synthetic basis images to obtain a compact feature descriptor. SYBA's performance and size are comparable to other binary feature descriptors. However, like other binary feature descriptors, SYBA's feature point matching accuracy suffers under large amounts of image variations.In this paper, we introduce an improved version of SYBA called robust-SYBA (rSYBA). rSYBA maintains the same reduced descriptor size and computational complexity as the original SYBA while increasing the number of correct matches and matching precision, especiall...

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