This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region...

The image space, scale and orientation domains can give valuable clues not seen in either individual of the domains. First we decomposed the face image into different orientation and scale by Gabor filter. Second, we combine local binary pattern analysis with Gabor. It gives a good face representation for recognition. Then we classify in discriminant based up on median histogram distance. The f...

In this paper, an efficient local operator, namely the Local Quantization Code (LQC), is proposed for texture classification. The conventional local binary pattern can be regarded as a special local quantization method with two levels, 0 and 1. Some variants of the LBP demonstrate that increasing the local quantization level can enhance the local discriminative capability. Hence, we present a s...

In this chapter, we propose two novel and curvature-free features: run-lengths of Local Binary Pattern (LBPruns) and Cloud Of Line Distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized images and gray scale images...

This paper presents an approach to derive critical points of a shape, the basis of a Reeb graph, using a combination of a medial axis skeleton and features along this skeleton. A Reeb graph captures the topology of a shape. The nodes in the graph represent critical points (positions of change in the topology), while edges represent topological persistence. We present an approach to compute such...

In this paper LBP and CM methods have been efficiently used for image retrieval for the content based image retrieval (CBIR) system. As LBP method may be sensible to noise in case of comparing neighboring pixels. The drawback of CM is it may be inefficient with too much details image. So it will be better to combine the feature of both method and utilized the property of both the two methods. I...

An effective remote sensing image scene classification approach using patch-based multi-scale completed local binary pattern (MS-CLBP) features and a Fisher vector (FV) is proposed. The approach extracts a set of local patch descriptors by partitioning an image and its multi-scale versions into dense patches and using the CLBP descriptor to characterize local rotation invariant texture informat...

The local primitives found in binary images are useful in the analysis and recognition of document and patent images. In this paper, an optimum detection of end points and junction points is obtained using morphological spurring and the granulometric curve of the image. A distance based algorithm is proposed to classify the local primitives found at the detected points. The size of the local re...

This paper presents a new Feature Local Binary Patterns (FLBP) that encodes the information of both local texture and features. The features are broadly defined by, for example, the edges, the Gabor wavelet features, the color features, etc. Specifically, a binary image is first derived by extracting feature pixels from a given image I, and then a distance vector field is obtained by computing ...

In this chapter, we propose two novel and curvature-free features: run-lengths of Local Binary Pattern (LBPruns) and Cloud Of Line Distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized images and gray scale images...