A local search approximation algorithm for k-means clustering
Proceedings of the eighteenth annual symposium on Computational geometry
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Introduction to MPEG-7: Multimedia Content Description Interface
Introduction to MPEG-7: Multimedia Content Description Interface
D-Index: Distance Searching Index for Metric Data Sets
Multimedia Tools and Applications
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Content-based sub-image retrieval using relevance feedback
Proceedings of the 2nd ACM international workshop on Multimedia databases
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Ranked subsequence matching in time-series databases
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Multi-probe LSH: efficient indexing for high-dimensional similarity search
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Implementation of the Smith-Waterman algorithm on a reconfigurable supercomputing platform
HPRCTA '07 Proceedings of the 1st international workshop on High-performance reconfigurable computing technology and applications: held in conjunction with SC07
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
A fully affine invariant image comparison method
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Logo retrieval with a contrario visual query expansion
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Metric Index: An Efficient and Scalable Solution for Similarity Search
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
SIFT-Based Image Retrieval Combining the Distance Measure of Global Image and Sub-Image
IIH-MSP '09 Proceedings of the 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Image retrieval using sub-image matching in photos using MPEG-7 descriptors
AIRS'05 Proceedings of the Second Asia conference on Asia Information Retrieval Technology
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The availability of various photo archives and photo sharing systems made similarity searching much more important because the photos are not usually conveniently tagged. So the photos images need to be searched by their content. Moreover, it is important not only to compare images with a query holistically but also to locate images that contain the query as their part. The query can be a picture of a person, building, or an abstract object and the task is to retrieve images of the query object but from a different perspective or images capturing a global scene containing the query object. This retrieval is called the sub-image searching. In this paper, the authors propose an algorithm, called SASISA, for retrieving database images by their similarity to and containment of a query. The novelty of it lies in application of a sequence alignment algorithm, which is commonly used in text retrieval. This forms an orthogonal solution to currently used approaches based on inverted files. They improve efficiency of SASISA by applying vector-quantization of local image feature descriptors. The proposed algorithm and its optimization are evaluated on a real-life data set containing photographs where images of logos are searched. It is compared to a state-of-the-art method Joly & Buisson, 2009 and the improvement of 16% in mean average precision mAP is obtained.