Object segmentation is an important preprocessing step for many target
recognition applications. Many segmentation methods have been studied,
but there is still no satisfactory effectiveness measure which makes
it hard to compare different segmentation methods, or even different
parameterizations of a single method. A good segmentation evaluation
method not only would enable different approaches to be compared, but
could also be integrated within the target recognition system to
adaptively select the appropriate granularity of the segmentation
which in turn could improve the recognition accuracy. A few
stand-alone effectiveness measures have been proposed, but these
measures examine different fundamental criteria of the objects, or
examine the same criteria in a different fashion, so they usually work
well in some cases, but poorly in the others. We propose a em
co-evaluation framework, in which different effectiveness measures
judge the performance of the segmentation in different ways, and their
measures are combined by using a machine learning approach which
coalesces the results. Experimental results demonstrate that our
method performs better than the existing methods.
In region-based image segmentation, an image is partitioned into connected regions by grouping neighboring pixels of similar features. To achieve fine-grain segmentation at the pixel level, we must be able to define features on a per-pixel basis. Typically for individual pixels, texture feature extraction is very computationally intensive. In this paper, we propose a new hierarchical method to reduce the computational complexity and expedite texture feature extraction, by taking advantage of the similarities between the neighboring pixels. In our method, an image is divided into blocks of pixels of different granularities at the various levels of the hierarchy. A representative pixel is used to describe the texture within a block. Each pixel within a block gets its texture feature values either by copying the corresponding representative pixel’s texture features, if its features are deemed sufficiently similar, or by computing its own texture features if it is a representative pixel itself. This way, we extract texture features for each pixel in the image with the minimal amount of texture feature extraction computation. The experiments demonstrate the good performance of our method, which can reduce 30% to 60% of the computational time while keeping the distortions in the range of 0.6% to 3.7%. By tailoring the texture feature extraction threshold, we can balance the tradeoff between extraction speed and distortion according to the each system’s specific needs.
Accurate image segmentation is important for many image, video and computer vision applications. Over the last few decades, many image segmentation methods have been proposed. However, the results of these segmentation methods are usually evaluated only visually, qualitatively, or indirectly by the effectiveness of the segmentation on the subsequent processing steps. Such methods are either subjective or tied to particular applications. They do not judge the performance of a segmentation method objectively, and cannot be used as a means to compare the performance of different segmentation techniques. A few quantitative evaluation methods have been proposed,
but these early methods have been based entirely on empirical analysis and have no theoretical grounding. In this paper, we propose a novel objective segmentation evaluation method based on information theory. The new method uses entropy as the basis for measuring the uniformity of pixel characteristics (luminance is used in this paper) within a segmentation region. The evaluation method provides a relative quality score that can be used to compare different segmentations of the same image. This method can be used to compare both various parameterizations of one particular segmentation method as well as fundamentally different segmentation techniques. The results from this preliminary study indicate that the proposed evaluation method is superior to the prior quantitative segmentation evaluation techniques, and identify areas for future research in objective segmentation evaluation.