BIM: Image matching using biological gene sequence alignment

Kim, Hung-Sik; Chang, Hau-Wen; Liu, Haibin; Lee, Jeong-Kyu; Lee, Dongwon

doi:10.1109/icip.2009.5414214

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Applying tools on strings to image matching is not new. A commonly used alignment tool for biological sequences has been used in matching images [20]. In another application, sequences generated from object boundaries are used in recognizing weapons from given images [2].…”

Section: Introductionmentioning

confidence: 99%

A new framework for subimage analysis

Arslan

2019

SN Appl. Sci.

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We develop new methods for several subimage analysis problems. Images in two dimensions are represented by matrixes (higher dimensional images are represented by higher dimensional arrays). This representation yields efficient algorithms for image search and comparison problems applicable not only to images, but also to array search and comparison in any dimension two or larger. Subimage analysis based on our array-representation has limitations. For example, our methods are not rotation and scale invariant. However, they yield efficient algorithms that have practical applications. Videos and snapshots taken from videos are continuously added to the digital world where they are permanently accessible. From a snapshot, one may want to locate and watch the part of the video that contains this snapshot (e.g. moments in news-footage, soccer or olympic games). One may also want to find common parts (sequences of frames) in multiple videos for various reasons (e.g. copyright-check, false-news detection). Our algorithms are applicable in these cases. For another application, we propose representing RNA secondary structures by matrixes. Since our methods are applicable to submatrix analysis, RNA substructure search and multiple RNA structure comparison problems can be solved by using our algorithms (for exact matches).

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Section: Introductionmentioning

confidence: 99%

A new framework for subimage analysis

Arslan

2019

SN Appl. Sci.

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“…Therefore, one can have an immediate access to a vast number of tools with successful transformation from NDID problem to gene sequence alignment problem. The preliminary BLAST-based algorithm was on [9], but simple conversion between image features and genes was only introduced. In addition, the algorithm addressed the CBIR problem, but not the NDID problem.…”

Section: Introductionmentioning

confidence: 99%

BASIL: Effective Near-Duplicate Image Detection Using Gene Sequence Alignment

Kim

Chang

Lee

et al. 2010

Lecture Notes in Computer Science

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Abstract. Finding near-duplicate images is a task often found in Multimedia Information Retrieval (MIR). Toward this effort, we propose a novel idea by bridging two seemingly unrelated fields -MIR and Biology. That is, we propose to use the popular gene sequence alignment algorithm in Biology, i.e., BLAST, in detecting near-duplicate images. Under the new idea, we study how various image features and gene sequence generation methods (using gene alphabets such as A, C, G, and T in DNA sequences) affect the accuracy and performance of detecting near-duplicate images. Our proposal, termed as BLASTed Image Linkage (BASIL), is empirically validated using various real data sets. This work can be viewed as the "first" step toward bridging MIR and Biology fields in the well-studied near-duplicate image detection problem.

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