Title :
Unsupervised Bitstream Based Segmentation of Images
Author :
Mecimore, Ivan ; Creusere, Charles D.
Author_Institution :
Klipsch Sch. of Electr. & Comput. Eng., New Mexico State Univ., Las Cruces, NM
Abstract :
We consider here image segmentation as a problem of clustering texture features by frequency content. Specifically, we develop a low complexity algorithm for image segmentation that operates directly on the bitstream of JPEG compressed images. Using morphological filtering and watersheds, the algorithm effectively segments an image by combining areas of similar frequency content. Its low complexity and the fact that it does not require decoding makes it well-suited for distributed wireless sensor networks and image database search applications.
Keywords :
data compression; feature extraction; filtering theory; image coding; image segmentation; image texture; pattern clustering; JPEG compressed image; frequency content; image segmentation; low complexity algorithm; morphological filtering; texture features clustering; unsupervised bitstream; watershed; Clustering algorithms; Decoding; Discrete cosine transforms; Filtering; Filters; Frequency; Image databases; Image segmentation; Pixel; Transform coding; JPEG-based image segmentation; bitstream processing; image segmentation; morphological filtering; sensor network coding;
Conference_Titel :
Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th
Conference_Location :
Marco Island, FL
Print_ISBN :
978-1-4244-3677-4
Electronic_ISBN :
978-1-4244-3677-4
DOI :
10.1109/DSP.2009.4786002
