Image Retrieval using Visual Phrases
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 3
Abstract
Keypoint based descriptors are widely used for various computer vision applications. During this process, key-points are initially detected from the given images which are later represented by some robust and distinctive descriptors like scale-invariant feature transform (SIFT). Keypoint based image-to-image matching has gained significant accuracy for image retrieval type of applications like image copy detection, similar image retrieval and near duplicate detection. Local keypoint descriptors are quantized into visual words to reduce the feature space which makes image-to-image matching possible for large scale applications. Bag of visual word quantization makes it efficient at the cost of accuracy. In this paper, the bag of visual word model is extended to detect frequent pair of visual words which is known as frequent item-set in text processing, also called visual phrases. Visual phrases increase the accuracy of image retrieval without increasing the vocabulary size. Experiments are carried out on benchmark datasets that depict the effectiveness of proposed scheme.
Authors and Affiliations
Benish Anwar, Junaid Baber, Atiq Ahmed, Maheen Bakhtyar, Sher Muhammad Daudpota, Anwar Ali Sanjrani, Ihsan Ullah
Keywords
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- EP ID EP499597
- DOI 10.14569/IJACSA.2019.0100361
- Views 97
- Downloads 0
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