Abstract
Automatic image classification is of major importance for a wide range of applications and is supported by a complex process that usually requires the identification of individual regions and spatial patterns (contextual information) among neighboring regions within images. Hierarchical conditional random fields (CRF) consider both multi-scale and contextual information in a unified discriminative probabilistic framework, yet they suffer from two main drawbacks. On the one hand, their current classification performance still leaves space for improvement, mostly due to the use of very simple or inappropriate pairwise energy expressions to model complex spatial patterns; on the other hand, their training remains complex, particularly for multi-class problems. In this work, we investigated alternative pairwise energy expressions to better account for class transitions and developed an efficient parameters learning strategy for the resultant expression. We propose: (i) a multi-scale CRF model with novel energies that involves information related to the multi-scale image structure; and (ii) an efficient maximum margin parameters learning procedure where the complex learning problem is decomposed into simpler individual multi-class sub-problems. During experiments conducted on several well-known satellite image data sets, the suggested multi-scale CRF exhibited between a 1% and 15% accuracy improvement compared to other works. We also found that, on different multi-scale decompositions, the total number of regions and their average size have a direct impact on the classification results.
Highlights
In this work, we consider the following problem: after segmenting an image into several regions, classify each region into one of the predefined classes
We considered values for d, such that ∀5m=1 dm ∈ R+ with d4 = d5 = 0 and solved the parameter learning in a max-margin approach, where each resultant problem of Equation (28) takes the form of a standard multiclass support vector machine (SVM) model, very similar to the work of Crammer and Singer [26], that can be efficiently solved dually using the fixed-point method
We selected the loopy believe propagation (LBP) method for the inference process, which approximate the marginalized posterior by a value named believes, computed using a message passing algorithm [44]
Summary
We consider the following problem: after segmenting an image into several regions, classify each region into one of the predefined classes. The first issue is that classification results, in several cases, are still very low We associate this problem with an inappropriate pairwise energy expression that fails to model complex spatial patterns (i.e., class transition detections or co-occurrence patterns). When using any of them in the context of CRF parameter learning, another challenge is related to the necessity of solving an inference problem at each iteration during the optimization loop, which increases the computational burden Other training strategies, such as the piecewise training framework [20] and its variants [21], proposed simpler formulations to the parameter learning problem and have been shown to be very competitive [19,21]. The second contribution of this work is a more general energy expression considering contextual and multi-scale information, in a max-margin parameter learning approach within the piecewise framework, which provides a novel strategy to tackle the resultant parameter learning problem.
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