An Unsupervised Segmentation Framework For Texture Image Queries
نویسندگان
چکیده
In this paper, a novel unsupervised segmentation framework for texture image queries is presented. The proposed framework consists of an unsupervised segmentation method for texture images, and a multi-filter query strategy. By applying the unsupervised segmentation method on each texture image, a set of texture feature parameters for that texture image can be extracted automatically. Based upon these parameters, an effective multi-filter query strategy which allows the users to issue texture-based image queries is developed. The test results of the proposed framework on 318 texture images obtained from the MIT VisTex and Brodatz database are presented to show its effectiveness.
منابع مشابه
Unsupervised Texture Image Segmentation Using MRFEM Framework
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
متن کاملUnsupervised Texture Image Segmentation Using MRFEM Framework
Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...
متن کاملUnsupervised Natural Image Segmentation Using Mean Histogram Features
A new histogram feature based natural image segmentation algorithm has been proposed. The proposed scheme uses histogram based new color texture extraction method which inherently combines color texture features rather then explicitly extracting it. A non parametric Bayesean clustering is employed to make the segmentation framework fully unsupervised where no a priori knowledge about the number...
متن کاملSIDF: A Novel Framework for Accurate Surgical Instrument Detection in Laparoscopic Video Frames
Background and Objectives: Identification of surgical instruments in laparoscopic video images has several biomedical applications. While several methods have been proposed for accurate detection of surgical instruments, the accuracy of these methods is still challenged high complexity of the laparoscopic video images. This paper introduces a Surgical Instrument Detection Framework (SIDF) for a...
متن کاملMultiscale Transforms for Region-based Texture Segmentation
In this paper we propose a rigorous and elegant framework for texture image segmentation relying on region-based active contours (RBAC), shape derivative tools and multiscale geometrical texture representations. After transforming the texture in a dictionary of appropriate waveforms (atoms), the obtained transform coefficients are intended to efficiently capture the essential spectral and geome...
متن کامل