Received Nov 3, 2016 Revised Jan 7, 2017 Accepted Feb 16, 2017 Retrieval of similar images from large dataset of brain images across patients would help the experts in the decision diagnosis process of diseases. Generally used feature extraction methods are color, texture and shape. In medical images texture and shape features are most efficient. Histogram of Oriented Gradients (HOG) and Local ...

BACKGROUND Classification of breast ultrasound (BUS) images is an important step in the computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel phase-based texture descriptor is proposed for efficient and robust classifiers to discriminate benign and malignant tumors in BUS images. METHOD The proposed descriptor, namely the phased congruency-based binary pattern (PCBP)...

In this study, we propose a simple yet very effective method for extracting color information through binary feature description framework. Our method expands the dimension of binary comparisons into RGB and YCbCr spaces, showing more than 100% matching improvement compared to non-color binary descriptors for a wide range of hard-to-match cases. The proposed method is general and can be applied...

Automatic recognition of facial expression is an active research topic in computer vision due to its importance in both human-computer and social interaction. One of the critical issues for a successful facial expression recognition system is to design a robust facial feature descriptor. Among the different existing methods, the Local Binary Pattern (LBP) has been proved to be a simple and effe...

In this paper we propose a novel texture recognition feature called Fractal Weighted Local Binary Pattern (FWLBP). It has been observed that fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human perception of surface roughness. We have utilized this property to construct a scale-invariant descriptor. We have sampled the input image u...

This paper proposes a novel approach to encode cross-channel texture correlation for color texture classification task. Firstly, we quantitatively study the correlation between different color channels using Local Binary Pattern (LBP) as the texture descriptor and using Shannon’s information theory to measure the correlation. We find that (R, G) channel pair exhibits stronger correlation than (...

This paper presents a local feature descriptor, the Local Distinctive Star Pattern (LDSP), for facial expression recognition. The feature is obtained from a local 3x3 pixels area by computing the directional edge response value. Each pixel is represented by two 4-bit binary patterns, which is named as LDSP feature for that pixel. Each face is divided into 81 equal sized blocks and histogram of ...

This paper presents an affine invariant shape descriptor which could be applied to both binary and gray-level images. The proposed algorithm uses gradient based features which are extracted along the object boundaries. We use two-dimensional steerable G-Filters [1] to obtain gradient information at different orientations. We aggregate the gradients into a shape signature. The signatures derived...

The traditional scale invariant feature transform (SIFT) method can extract distinctive features for image matching. However, it is extremely time-consuming in SIFT matching because of the use of the Euclidean distance measure. Recently, many binary SIFT (BSIFT) methods have been developed to improve matching efficiency; however, none of them is invariant to mirror reflection. To address these ...

The local descriptors have been the backbone of most of the computer vision problems. Most of the existing local descriptors are generated over the raw input images. In order to increase the discriminative power of the local descriptors, some researchers converted the raw image into multiple images with the help some high and low pass frequency filters, then the local descriptors are computed o...