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Texture feature captures the structural arrangement details of pixels present in a given image. The local binary pattern (LBP) and its modified versions have been used to extract discriminant information about the structural arrangement. A unique textural representation obtained from LBP and its modified versions are affected by rotating the structural arrangement of the pixels available in a particular region. The weight of the binary values obtained from the LBP and its modified versions are constant. The structural arrangement of the local region is effectively determined from the direction-oriented edge information [i.e., local directional pattern (LDP)]. The LDP is also nonrobust toward the structural arrangement changes in the local region. We give a direction-invariant binary pattern to represent the texture details available in a particular location by encoding edge response along with the LBP of that region. The direction-invariant property is derived from the weight of the edge response available in a particular region. The edge response around each local region of the image is captured using the compass edge detector. It detects the edge details from eight directions. The process of encoding eight direction edge responses along with a binary pattern obtained from the center pixel difference gives a more robust texture descriptor than the other rotation-invariant descriptors used in texture feature extraction. The proposed descriptor is tested over five standard databases, namely Brodatz, color Brodatz, KTH-TIPS2b, FTVL, and free photo. The performance of the proposed rotation-invariant texture feature is more effective than the existing rotation-invariant local texture feature representations involved in the image classification and retrieval experiments.

This paper proposes a robust method based on a local intensity binary pattern for interest feature description. To avoid estimating reference orientation, local features were calculated on a rotation invariant system. Different from the local binary patterns (LBP) and center-symmetric-LBP operator, our proposed local circular contrast pattern (LCCP) operator calculates a local binary feature by comparing the relative intensity order information of each two adjacent elements in the sequence consisting of the sampling point and its neighboring points. To evaluate the performance of our proposed descriptor LCCP and other existing descriptors (e.g., scale-invariant feature transform, DAISY, HRI-CSLTP, multisupport region order-based gradient histogram-single, local intensity order pattern), image matching experiments were first conducted on the Oxford dataset, additional image pairs with complex light changes, image sequences with different noise, and three-dimensional objects dataset. To further evaluate the discriminative ability of local descriptors, a simple object recognition experiment was carried out on three public datasets. The experimental results show that our descriptor LCCP exhibits a better performance and robustness than other evaluated descriptors.

Binary image descriptors have received a lot of attention in recent years, since they provide numerous advantages, such as low memory footprint and efficient matching strategy. However, they utilize intermediate representations and are generally less discriminative than floating-point descriptors. We propose an image region descriptor, namely local derivative ordinal binary pattern, for object recognition and image categorization. In order to preserve more local contrast and edge information, we quantize the intensity differences between the central pixels and their neighbors of the detected local affine covariant regions in an adaptive way. These differences are then sorted and mapped into binary codes and histogrammed with a weight of the sum of the absolute value of the differences. Furthermore, the gray level of the central pixel is quantized to further improve the discriminative ability. Finally, we combine them to form a joint histogram to represent the features of the image. We observe that our descriptor preserves more local brightness and edge information than traditional binary descriptors. Also, our descriptor is robust to rotation, illumination variations, and other geometric transformations. We conduct extensive experiments on the standard ETHZ and Kentucky datasets for object recognition and PASCAL for image classification. The experimental results show that our descriptor outperforms existing state-of-the-art methods.