Jaime Melendez

Unsupervised texture-based image segmentation through pattern discovery

This paper presents a new efficient technique for unsupervised segmentation of textured images that aims at incorporating the advantages of supervision for discriminating texture patterns. First, a pattern discovery stage that relies on a clustering algorithm is utilized for determining the texture patterns of a given image based on the outcome of a multichannel Gabor filter bank. Then, a supervised pixel-based classifier trained with the feature vectors associated with those patterns is used to classify every image pixel into one of the sought texture classes, thus yielding the final segmentation. Multi-sized evaluation windows following a top-down approach are utilized during pixel classification in order to improve accuracy both inside and near boundaries of regions of homogeneous texture. Results with synthetic compositions and with complex real images are presented and discussed. The proposed technique is also compared with alternative texture segmentation approaches.

Application-independent feature selection for texture classification

Recent developments in texture classification have shown that the proper integration of texture methods from different families leads to significant improvements in terms of classification rate compared to the use of a single family of texture methods. In order to reduce the computational burden of that integration process, a selection stage is necessary. In general, a large number of feature selection techniques have been proposed. However, a specific texture feature selection must be typically applied given a particular set of texture patterns to be classified. This paper describes a new texture feature selection algorithm that is independent of specific classification problems/applications and thus must only be run once given a set of available texture methods. The proposed application-independent selection scheme has been evaluated and compared to previous proposals on both Brodatz compositions and complex real images.

Efficient distance-based per-pixel texture classification with Gabor wavelet filters

This paper proposes an efficient solution to the problem of per-pixel classification of textured images with multichannel Gabor wavelet filters based on a selection scheme that automatically determines a subset of prototypes that characterize each texture class. Results with Brodatz compositions and outdoor images, and comparisons with alternative classification techniques are presented.

Multi-level pixel-based texture classification through efficient prototype selection via normalized cut

This paper presents a new efficient technique for supervised pixel-based classification of textured images. A prototype selection algorithm that relies on the normalized cut criterion is utilized for automatically determining a subset of prototypes in order to characterize each texture class at the local level based on the outcome of a multichannel Gabor filter bank. Then, a simple minimum distance classifier fed with the previously determined prototypes is used to classify every image pixel into one of the given texture classes. Multi-sized evaluation windows following a top-down approach are used during classification in order to improve accuracy near frontiers of regions of different texture. Results with standard Brodatz, VisTex and MeasTex compositions and with complex real images are presented and discussed. The proposed technique is also compared with alternative texture classifiers.

Comparative evaluation of classical methods, optimized gabor filters and LBP for texture feature selection and classification

This paper builds upon a previous texture feature selection and classification methodology by extending it with two state-of-the-art families of texture feature extraction methods, namely Manjunath & Mas Gabor wavelet filters and Local Binary Pattern operators (LBP), which are integrated with more classical families of texture filters, such as co-occurrence matrices, Laws filters and wavelet transforms. Results with Brodatz compositions and outdoor images are evaluated and discussed, being the basis for a comparative study about the discrimination capabilities of those different families of texture methods, which have been traditionally applied on their own.

On Adapting Pixel-based Classification to Unsupervised Texture Segmentation

An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated with a set of texture patterns initially extracted through a clustering algorithm. Therefore, the final segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluation windows following a top-down approach are applied during pixel classification in order to improve accuracy. The proposed technique has been experimentally validated on MeasTex, VisTex and Brodatz compositions, as well as on complex ground and aerial outdoor images. Comparisons with state-of the-art unsupervised texture segmenters are also provided.

Gabor-based texture classification through efficient prototype selection via normalized cut

This paper presents a new efficient technique for supervised pixel-based texture classification. The proposed scheme first performs a selection process that automatically determines a subset of prototypes that characterize each texture class based on the outcome of a multichannel Gabor wavelet filter bank. Then, every image pixel is classified into one of the given texture classes by using a K-NN classifier fed with the prototypes determined previously. The proposed technique is compared to previous texture classifiers by using both Brodatz and real outdoor textured images.