Abstract
The identification and retrieval of images of same or similar objects finds application in various tasks that are of prime importance in Image Processing and Computer Vision. Accurate and fast extraction of the object of interest from several images is essential for the construction of 3D models, image retrieval applications. The joint partitioning of multiple images having same or similar objects of interest into background and foreground parts is referred to as co-segmentation.
This article proposes a novel and efficient interactive co-segmentation method based on the computation of a global energy function and a local smooth energy function. Computation of global energy function from the scribbled regions of the images is based on histogram matching. This is used to estimate the probability of each region belonging either to foreground or background region. The local smooth energy function is used to estimate the probability of regions having similar colour appearance. To further improve the quality of the segmentation, a bipartite graph is constructed using the segments. The algorithm has been implemented on iCoseg and MSRC benchmark data sets. The extensive experimental results show significant improvement in performance compared to many state-of-the-art unsupervised co-segmentation and supervised interactive co-segmentation methods, both in computational time and accuracy.
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Bhandari, H., Palit, S., Chanda, B. (2020). Supervised Interactive Co-segmentation Using Histogram Matching and Bipartite Graph Construction. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_23
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