Efficient and robust near-duplicate detection in large and growing image data-sets

Abstract

Due to the increasing flood of digital images and the overall increase of storage capacity, large scale image databases are common these days. This work deals with the problem of finding replicas in image databases containing more than 100000 images. A clustering algorithm is developed that has linear runtime and can be carried out in parallel. We observe that with increasing size of the database, the problem of decreasing discrimination between high frequency images arises. Features of images with natural repetitive texture become similar to other images and show up in most of the search results. This problem is addressed by developing an asymmetric Hamming distance measurement for bags of visual words. It allows better discrimination power in large databases, while being robust to image transformations such as rotation, cropping, or change of resolution and size.

A Review on Near-Duplicate Detection of Images using Computer Vision Techniques

“…Thomas and Julian Stottinger has a linear runtime and can be carried out in parallel [86]. They show that the quality of image retrieval depends on the quantization.…”

“…Thomas and Julian Stottinger has a linear runtime and can be carried out in parallel [86]. shear (4).…”

An Image Similarity Acceleration Detection Algorithm Based on Sparse Coding

“…(4) Search for the nearest column coefficients of the ScSIFT feature according to the index index. (5) Calculate the distance of for each column and according to formula (6). If ≤ , then the feature matching amount ∑ of the image which the column belongs to is increased by 1.…”

“…In the literature [4], a SIFT feature filtering algorithm is proposed, which effectively reduces the SIFT feature points extracted from an image by the punishment mechanism, reduces the computational complexity, and improves the matching accuracy of the SIFT algorithm. In [5,6], the image is classified into different categories by using the clustering algorithm. It calculates the distance between the image to be detected and different clustering center and selects the nearest several types of images and exactly matches their SIFT characters, which reduces the SIFT feature matching data and improves the detection speed of the algorithm.…”