With the rapid growth of Web images

image retrieval is becoming an important application.However

Web images are easily downloaded

edited

and re-uploaded.Therefore

a great number of image copies on the Web can be found.Finding and filtering image copies can improve the effectiveness of image search engines.Two images are considered as copy in this study based on whether these two images are generated from the same original image by certain image editing operations.Editing operations include cropping

scaling

rotating

changing the compression rate

adding other contents

framing

and other non-affine geometric transformations.Over the last few years

several methods have been proposed to detect image copy in large-scale image dataset.In the early stage of image copy detection research

an entire image is represented as a feature vector or descriptor.The similarity of feature vector or descriptor is measured to verify whether two images are copies.These methods are efficient and do not require high storage cost.However

these methods are not robust to certain common image editing operations

such as chopping and adding of objects.Local feature is more robust in image copy detection compared with global feature.However

local feature

such as SIFT

is a high-dimension feature vector and result in high time cost in local feature matching

especially in large-scale image dataset.The bag-of-words (BoW) model was applied to image copy detection field and used by state-of-the-art methods on image copy detection to solve the aforementioned problem.In these methods

an image is represented as a bag of local features

which are then quantized into visual words.Inverted file indexing is applied to register images via these visual words and improves retrieval efficiency.However

visual words have significantly lesser discriminative power than text words because of quantization loss.The loss of quantification on local feature causes a large number of mismatching local features

which affect the precision of image copy detection.Some methods have been proposed to eliminate the influence of visual word mismatches and improve image copy detection performance.The geometric verification for rejecting visual word mismatches has become popular as a visual words post-verification step.However

geometric verification methods initially need to obtain the matched pairs of visual words between query and candidate images.Then

the spatial similarity of the matched visual words between the two images is calculated to reject mismatches of visual words.The process of rejecting mismatches is usually applied to only some top-ranked candidate images because of due to expensive computational cost and large number of candidate images in large-scale datasets.In addressing the problems of post-verification processes of visual words

one basic idea that has been explored is designing a contextual descriptor that can be used to filter the mismatches of visual words immediately according to the similarity of descriptors.An image copy detection method based on contextual descriptor is proposed in this paper.The contextual descriptor consists of the information regarding the neighboring local features and improves the discriminative power of visual words. In the proposed method

the information about the contextual descriptor consists of neighboring visual words and the spatial relations of local features.The neighboring visual words represent the neighboring local features

whereas the spatial relations are represented as angles in the contextual descriptor.If the matching visual words have similar neighboring local feature

then the pair of visual word is considered as a true match.The process of the proposed method is as follows.The Euclidean distance and scale of the local feature are used as the context of a local feature in an image to select the neighbors.The information about neighbors

such as position

dominant orientation

and visual words are used to construct the contextual descriptor.Subsequently

each candidate match of the local feature is verified according to the similarity measure of the contextual descriptor whether it is a true match.In this measure

if the matching visual words have the same neighbors and similar spatial relations

then the matching pair of visual words is considered as a true match.Finally

the similarity between images is measured by the number of true matches of visual words.In this study

neighboring visual words and spatial relations are used to verify matching visual words.The verification measure is significantly strict.Most mismatched visual words are eliminated. Some experiments are performed on the Copydays database and compared the proposed method with the baseline method.Experiments show that the mean average precision (mAP) of the proposed method increased by 63% compared with the baseline method

whereas the distracter images are 100 k.Although the distracter images increased from 100 k to 1 M

the experimental results of the baseline still decreased by 9% and the proposed method only decreased by 3%.In the proposed method

neighboring visual words are indexed into the contextual descriptor before image copy detection.Thus

the proposed method belongs to the pre-verification method and has less detection time compared with the post-verification method

which is confirmed by the experimental results. In this study

an image copy detection method that is robust to most image editing operations

such as rotation

image overlay

scale

and cropping

is proposed.The proposed method obtains high mAP in public test dataset and is efficient in real application scene

such as image copyright prevention and image duplication removal.