Posterior Accuracy Research Articles

A posterior evaluation algorithm of steganalysis accuracy inspired by residual co-occurrence probability

Steganalysis research is committed to distinguishing the steganography media from the normal one correctly. However, individual differences of carriers disturb the detection inevitably and greatly. Existing methods treat all detection results with the same confidence level, or prior accuracy, which may make the prior accuracy overestimate or underestimate the real result. This paper presents a novel performance evaluation method of steganalysis based on posterior accuracy. Adaptive Convolution Feature (ACF) is calculated by the adaptive convolution, then a quantitative value S based on the ACF is modeled to posterior testify and estimate the detection confidence of the image under test. By clustering of carrier noise, we classify the images which we believe they have a similar confidence of the same cluster. The distribution of ACF from each cluster indicates that the S value works well for confidence evaluation. The experimental results show that the S value can distinguish and identify high-confidence samples from the low-confidence, which will greatly improve the real performance of steganalysis. Besides, it improves the steganalysis accuracy of the whole image set by matching S values between training and prediction samples.

Algorithm for the discrete Weber’s problem with an accuracy estimate

We consider a relaxation of the quadratic assignment problem without the constraint on the number of objects assigned to a specific position. This problem is NP-hard in the general case. To solve the problem, we propose a polynomial algorithm with guaranteed posterior accuracy estimate; we distinguish a class of problems with special assignment cost functions where the algorithm is 2-approximate. We show that if the graph in question contains one simple loop, and the set of assignment positions is a metric space, the proposed algorithm is 2-approximate and guaranteed to be asymptotically exact. We conduct a computational experiment in order to analyze the algorithm's errors and evaluate its accuracy.

Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African floodplain

The use of satellite imagery data to accurately map agricultural land use in semi-arid Africa has proven difficult due to the small size of cropped fields, low vegetative cover within and outside cropped areas, and high correlation between the seasonal trajectories in the signatures of cropped and uncropped surfaces. This paper evaluates the potential of computer-aided classification of multi-temporal imagery data to map traditionally-managed rice fields in Sahelian West Africa. SPOT-XS imagery data covering the southwestern corner of the Inland Niger Delta of Mali were acquired on three dates in 1988. These dates encompass the pre-flood period when ploughed surfaces contrast strongly with surrounding land surface and the first half of the flood period. Two alternative classification approaches were evaluated. Supervised and unsupervised classification of a database formed by principal component reduction proved unsuccessful as a result of the high spectral heterogeneity of ploughed and unploughed surfaces. An alternative classification approach was developed which utilizes a series of steps (unsupervised classification, stratification, and supervised classification) that progressively reduce image heterogeneity in such a way to better aid interpretation and comparison with training data. Seventy-one per cent of ploughed-field reference sites were classified correctly using this approach, while 8-20 per cent of reference sites for cover types with a potential for being confused with ploughed fields were misclassified as ploughed fields. A fuller evaluation was performed using a posterior accuracy assessment of classifications produced at five intermediate stages of the procedure. The suitability of the procedure for land-use classification in spectrally-heterogenous areas is discussed.