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.
Highlights
Information extraction from the images is a very important process in image processing and computer vision
In order to overcome above mentioned issues in scale-invariant feature transform (SIFT) descriptor local keypoint features, local key descriptors are quantized using Bag of Visual Words (BoVW) technique
To detect frequent item-set, called as visual phrases in this paper, each keypoint descriptor is mapped to a visual word which is treated as an item
Summary
Information extraction from the images is a very important process in image processing and computer vision. One of the key issues is to search the visual information and phrases with computer vision techniques for image retrieval data from a huge database. In order to overcome above mentioned issues in SIFT descriptor local keypoint features, local key descriptors are quantized using Bag of Visual Words (BoVW) technique. Various quantization techniques are used for image processing and retrieval databases like, Fisher Vector [6], VLAD [7,8,9], binary quantizer and BoVW model [10]. With those limitations and evaluations of the proposed descriptors are quantized to represent features to the visual model are presented with a short conclusion in the last section. With those limitations and evaluations of the proposed descriptors are quantized to represent features to the visual model are presented with a short conclusion in the last section. word in smaller discrete size corpus vocabulary
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