Object instance recognition using best increasing subsequence

Harjono, Kristopher David; Kusuma, Gede Putra

doi:10.1109/kicss.2016.7951432

Cited by 2 publications

(1 citation statement)

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“…Kusuma et al proposed Object Recognition using Weighted Longest Increasing Subsequence [3]. Xie et al proposed Dense Feature extraction using SIFT and pose base verification [4], Best Increasing Subsequence (BIS) and image matching for Object Instance Recognition is proposed by Kusuma and Harjono [5] and the development of BIS which is Best Score Increasing Subsequence (BSIS) using SURF for feature extraction and image matching is proposed by Kusuma et al [6]. Meanwhile, there are also deep learning methods for Object Instance Recognition, such as Held et al proposed feedforward neural network for a single image [7].…”

Section: This Paper Focuses On Proposing a Methods For Object Instancementioning

confidence: 99%

Combination of Salient Object Detection and Image Matching for Object Instance Recognition

Wigati¹,

Kusuma²,

Utomo³

2020

Adv. sci. technol. eng. syst. j.

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Object Instance Recognition aims to classify objects specifically and usually use a single reference image. It is possible to be used in many applications such as visual search, information retrieval and augmented reality. However, various things affect the appearance of the objects, which makes the recognition process harder, especially if a single reference image is used. In this paper, we proposed a combination method between Salient Object Detection and Object Instance Recognition using Image Matching and Geometric Verification. Salient Object Detection is used during initial processing (feature extraction), while Geometric Verification is performed using Best Score Increasing Subsequence (BSIS). Experimental results showed that the Fβ score and Mean Absolute Error (MAE) of saliency map on Stanford Mobile Visual Search Dataset (SMVS) are quite satisfactory. While the results of the combination method show 1.92% performance improvement than the previous method which is BSIS without Salient Object Detection.

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Section: This Paper Focuses On Proposing a Methods For Object Instancementioning

confidence: 99%