Ranking by K-Means Voting Algorithm for Similar Image Retrieval

Górecki, Przemysław; Sopyła, Krzysztof; Drozda, Paweł

doi:10.1007/978-3-642-29347-4_59

Cited by 12 publications

(6 citation statements)

References 16 publications

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“…The presented method is based on three well-known algorithms: SURF [25][26][27], Mean Shift and K-Means. In this paper we compare efficiency of clustering algorithms in the field of visual object extraction [28,29].…”

Section: Presented Methods For Image Keypoint Clusteringmentioning

confidence: 99%

Novel visual object descriptor using SURF and clustering algorithms

Grycuk

2016

J. Appl. Math. Comput. Mech.

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Abstract. In this paper we propose a method for object description based on two wellknown clustering algorithms (k-means and mean shift) and the SURF method for keypoints detection. We also perform a comparison of these clustering methods in object description area. Both of these algorithms require one input parameter; k-means (k, number of objects) and mean shift (h, window). Our approach is suitable for images with a non-homogeneous background thus, the algorithm can be used not only on trivial images. In the future we will try to remove non-important keypoints detected by the SURF algorithm. Our method is a part of a larger CBIR system and it is used as a preprocessing stage.

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Section: Presented Methods For Image Keypoint Clusteringmentioning

confidence: 99%

Novel visual object descriptor using SURF and clustering algorithms

Grycuk

2016

J. Appl. Math. Comput. Mech.

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“…Worldwide highlights calculations are commonly viewed as straightforward and quick, which regularly brings about the absence of in-variance to change of point of view or light. To beat these issues local features techniques were presented Górecki et al [2012]. For example, Schmid Schmid and Mohr [1997] use Harris corner detector to distinguish intrigue focuses which is insensitive toward change of image direction.…”

Section: Image Copy Detectionmentioning

confidence: 99%

Compact Binary Fingerprint for Image Copy Re-Ranking

Mohammad,

Baber,

Bakhtyar

et al. 2021

Preprint

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Image copy detection is challenging and appealing topic in computer vision and signal processing. Recent advancements in multimedia have made distribution of image across the global easy and fast: that leads to many other issues such as forgery and image copy retrieval.Local keypoint descriptors such as SIFT are used to represent the images, and based on those descriptors matching, images are matched and retrieved. Features are quantized so that searching/matching may be made feasible for large databases at the cost of accuracy loss. In this paper, we propose binary feature that is obtained by quantizing the SIFT into binary, and rank list is re-examined to remove the false positives. Experiments on challenging dataset shows the gain in accuracy and time.

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“…Intuitively, more larger than 0 result means that the two images are far more different each other. Euclidean distance was used as similarity metric in similar scenario [14].…”

Section: Image Similarity Measurementmentioning

confidence: 99%

Images Similarity based on Bags of SIFT Descriptor and K-Means Clustering

Yohanes

2019

tech

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The content based image retrieval is developed and receives many attention from computer vision, supported by the ubiquity of Internet and digital devices. Bag-of-words method from text-based image retrieval trains images’ local features to build visual vocabulary. These visual words are used to represent local features, then quantized before clustering into number of bags. Here, the scale invariant feature transform descriptor is used as local features of images that will be compared each other to find their similarity. It is robust to clutter and partial visibility compared to global feature. The main goal of this research is to build and use a vocabolary to measure image similarity accross two tiny image datasets. K-means clustering algorithm is used to find the centroid of each cluster at different k values. From experiment results, the bag-of-keypoints method has potential to be implemented in the content based information retrieval.

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Ranking by K-Means Voting Algorithm for Similar Image Retrieval

Cited by 12 publications

References 16 publications

Novel visual object descriptor using SURF and clustering algorithms

Novel visual object descriptor using SURF and clustering algorithms

Compact Binary Fingerprint for Image Copy Re-Ranking

Images Similarity based on Bags of SIFT Descriptor and K-Means Clustering

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