Title :
Spatially related image mining on very large image collections
Author :
Emymal, S. Nancy ; Rubavathi, C. Yesubai
Author_Institution :
Francis Xavier Eng. Coll., Tirunelveli, India
Abstract :
Collections of images of ever growing sizes are becoming common. Structuring and browsing large image databases is a challenging problem. Access to images based on the 3D acquisition location or on the spatial overlap of the scenes they depict is intuitive and has high user acceptability. Commonly, the sets of relevant spatially related images are obtained using manual annotations. A method for discovering spatial overlaps using image content only via image retrieval techniques was proposed. The objective of the proposed approach is to provide a randomized data mining method for finding clusters of images with spatial overlap. Instead of trying to match each image, in turn, the method relies on the min- Hash algorithm for fast detection of random pairs of images with spatial overlap, the so-called cluster seeds. The seeds are then used as visual queries and clusters are obtained as transitive closures of sets of partially overlapping images that include the seed. This approach shows that the probability of finding a seed for an image cluster rapidly increases with the size of the cluster and approaches one fast. For practical database sizes, the running time of the seed generation process is close to linear in the size of the database. The cluster completion process requires a number of visual queries proportional to the number of images in all clusters. The proposed method discovers the spatially related images in large-scale image databases.
Keywords :
data mining; image retrieval; visual databases; 3D acquisition location; cluster seeds; image cluster; image content; image databases; image retrieval; min-hash algorithm; randomized data mining; spatial overlaps; spatially related image mining; very large image collections; visual queries; Data mining; Image edge detection; Image retrieval; Spatial databases; Visualization; Vocabulary; bag of words; cluster seed; image clustering; image retrieval; min Hash;
Conference_Titel :
Emerging Trends in Electrical and Computer Technology (ICETECT), 2011 International Conference on
Conference_Location :
Tamil Nadu
Print_ISBN :
978-1-4244-7923-8
DOI :
10.1109/ICETECT.2011.5760240