A probabilistic framework for unsupervised evaluation and ranking of image segmentations

In this paper, a Bayesian Network (BN) framework for unsupervised evaluation of image segmentation quality is proposed. This image understanding algorithm utilizes a set of given Segmentation Maps (SMs) ranging from under-segmented to over-segmented results for a target image, to identify the semantically meaningful ones and rank the SMs according to their applicability in image processing and computer vision systems. Images acquired from the Berkeley segmentation dataset along with their corresponding SMs are used to train and test the proposed algorithm. Low-level local and global image features are employed to define an optimal BN structure and to estimate the inference between its nodes. Furthermore, given several SMs of a test image, the optimal BN is utilized to estimate the probability that a given map is the most favorable segmentation for that image. The algorithm is evaluated on a separate set of images (none of which are included in the training set) wherein the ranked SMs (according to their probabilities of being acceptable segmentation as estimated by the proposed algorithm) are compared to the ground-truth maps generated by human observers. The Normalized Probabilistic Rand (NPR) index is used as an objective metric to quantify our algorithm's performance. The proposed algorithm is designed to serve as a pre-processing module in various bottom-up image processing frameworks such as content-based image retrieval and region-of-interest detection.

[1]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[2]  S. Kotsiantis,et al.  Discretization Techniques: A recent survey , 2006 .

[3]  B. S. Manjunath,et al.  Modeling and Detection of Geospatial Objects Using Texture Motifs , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[4]  S. Mitra,et al.  Color Image Segmentation : A State-ofthe-Art Survey , 2022 .

[5]  Kim L. Boyer,et al.  Classifying land development in high-resolution panchromatic satellite images using straight-line statistics , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Sylvie Philipp-Foliguet,et al.  Multi-Scale Criteria for the Evaluation of Image Segmentation Algorithms , 2008, J. Multim..

[7]  Anil M. Cheriyadat,et al.  Overhead image statistics , 2008, 2008 37th IEEE Applied Imagery Pattern Recognition Workshop.

[8]  Hélène Laurent,et al.  Optimization-Based Image Segmentation by Genetic Algorithms , 2008, EURASIP J. Image Video Process..

[9]  Mihai Datcu,et al.  Semantic Annotation of Satellite Images Using Latent Dirichlet Allocation , 2010, IEEE Geoscience and Remote Sensing Letters.

[10]  Jing Li Wang,et al.  Color image segmentation: advances and prospects , 2001, Pattern Recognit..

[11]  Martial Hebert,et al.  Toward Objective Evaluation of Image Segmentation Algorithms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Mark Q. Shaw,et al.  Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging , 2009, IEEE Transactions on Image Processing.

[13]  Eli Saber,et al.  Extraction of memory colors using Bayesian Networks , 2009, 2009 IEEE International Conference on System of Systems Engineering (SoSE).

[14]  Jake Porway,et al.  A Hierarchical and Contextual Model for Aerial Image Parsing , 2010, International Journal of Computer Vision.

[15]  Martial Hebert,et al.  Discriminative Random Fields , 2006, International Journal of Computer Vision.

[16]  Hsien-Che Lee,et al.  Detecting boundaries in a vector field , 1991, IEEE Trans. Signal Process..

[17]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.