This chapter presents a novel and generic framework for image segmentation using a compound image descriptor that encompasses both colour and texture information in an adaptive fashion. The developed image segmentation method extracts the texture information using low-level image descriptors (such as the Local Binary Patterns (LBP)) and colour information by using colour space partitioning. The...

Several approaches to object recognition make extensive use of local image information extracted in interest points, known as local image descriptors. State-of-the-art methods perform a statistical analysis of the gradient information around the interest point, which often relies on the computation of image derivatives with pixel differencing methods. In this paper we show the advantages of usi...

Application • A novel image feature descriptor, unit statistical curvature feature (USCF), is proposed based on the statistics of unit curvature distribution to represent the local general invariant features of the image texture. • USCF algorithm had high recognition rate for object images in any size including tiny object images. • USCF is invariant to rotation and linear illumination variatio...

We present a novel image feature descriptor for rotationally invariant 2D texture classification. This extends our previous work on noise-resistant and intensity-shift invariant median binary patterns (MBPs), which use binary pattern vectors based on adaptive median thresholding. In this paper the MBPs are hashed to a binary chain or equivalence class using a circular bit-shift operator. One bi...

This paper proposes a novel method to deal with the representation issue in texture classification. A learning framework of image descriptor is designed based on the Fisher separation criteria (FSC) to learn most reliable and robust dominant pattern types considering intraclass similarity and inter-class distance. Image structures are thus be described by a new FSC-based learning (FBL) encoding...

0167-8655/$ see front matter 2013 Elsevier B.V. A http://dx.doi.org/10.1016/j.patrec.2013.03.021 ⇑ Corresponding author. Tel./fax: +86 29 82667836 E-mail addresses: [email protected], liugz@xjtu Constructing proper descriptors for interest points in images is a critical aspect for local features related tasks in computer vision and pattern recognition. Although the SIFT descriptor has been p...

The automatic classification of the HEp-2 cell stain patterns from indirect immunofluorescence images has attracted much attention recently. As an image classification problem, it can be well solved by the state-of-theart bag-of-features (BoF) model as long as a suitable local descriptor is known. Unfortunately, for this special task, we have very limited knowledge of such a descriptor. In this...

Scale Invariant Feature Transform is a widely used image descriptor, which is distinctive and robust in real-world applications. However, the high dimensionality of this descriptor causes computational inefficiency when there are a large number of points to be processed. This problem has led to several attempts at developing more compact SIFT-like descriptors, which are suitable for faster matc...

This paper proposes a new feature descriptor, localized color descriptor for content based image retrieval (CBIR). The proposed method collects the local histograms from red (R), green (G) and blue (B) color spaces. These local histograms are collected by dividing the images into subblocks (regions). The performance of the proposed method is tested by conducting experiments on Corel-1000, natur...

Recently, Spatial Pyramid Matching (SPM) with nonlinear coding strategies, e.g., sparse code based SPM (ScSPM) and locality-constrained linear coding (LLC), have achieved a lot of success in image classification. Although these methods achieve a higher recognition rate and take less time for classification than the traditional SPM, they consume more time to encode each local descriptor extracte...