Abstract
An image can be represented by different types of feature descriptors. Each descriptor holds specific details about the image. The image classification and retrieval applications use these extracted features to classify the query image and retrieve similar kinds of images presented in huge databases for the given query image. The discriminant feature in the form of a binary pattern available around each pixel of the image is extracted by taking the local pixel difference between each pixel and its neighboring pixels (i.e., sampling points) present in different radii. The discrete wavelet representation (i.e., multi-resolution) of the image has more information about the image compared to the image present in the spatial domain since each level of the multi-level decomposition discloses significant details about the image in separate channels. However, binary pattern extraction around each coefficient of the different levels of decomposed images lacks in producing the discriminant texture feature representation since it extracts a feature from each decomposition level at a time and concatenates them. Thus, the proposed work extracts the binary pattern around each coefficient of different levels of decomposed image from different radii. Then, the obtained binary patterns available in different decomposition levels are encoded to represent the discriminant texture feature around each pixel location. Consequently, the proposed work selects the multi-level decomposition based on the stationary wavelet transform, since it has the same resolution in each level of decomposition. The performance of the proposed feature descriptor is assessed over seven different sets of databases using image classification and image retrieval applications. Finally, the performance of the proposed work is compared with the state-of-the-art techniques involved in extracting spatial arrangement details of pixels available in the image.
Similar content being viewed by others
References
Cutzu, F., Hammoud, R., & Leykin, A. (2005). Distinguishing paintings from photographs. Computer Vision and Image Understanding, 100(3), 249–273.
Dubey, S. R., Singh, S. K., & Singh, R. K. (2016). Multichannel decoded local binary patterns for content-based image retrieval. IEEE Transactions on Image Processing, 25(9), 4018–4032.
Hafiane, A., Palaniappan, K., & Seetharaman, G. (2015). Joint adaptive median binary patterns for texture classification. Pattern Recognition, 48, 2609–2620.
Haralick, R., Shanmugam, K., & Dinstein, I. (1973). Texture features for image classification. IEEE Transaction, SMC-3(6), 610–621.
Hu, L., Ji, Y., Li, Y., & Gao, F. (2010). SAR image segmentation based on Kullback-Leibler distance of edgeworth. In G. Qiu, K. M. Lam, H. Kiya, X. Y. Xue, C. C. J. Kuo, & M. S. Lew (Eds.), Advances in multimedia information processing. PCM 2010. Lecture Notes in Computer Science (Vol. 6297). Heidelberg: Springer.
Li, J., & Wang, J. Z. (2003). Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(9), 1075–1088.
Khare, M., Srivastava, P., Gwak, J., & Khare, A. (2018). A multiresolution approach for content-based image retrieval using wavelet transform of local binary pattern. In N. Nguyen, D. Hoang, T. P. Hong, H. Pham, & B. Trawiński (Eds.), Intelligent information and database systems. ACIIDS 2018. Lecture Notes in Computer Science (Vol. 10752). Cham: Springer.
Kokare, M., Biswas, P. K., & Chatterji, B. N. (2006). Rotation-invariant texture image retrieval using rotated complex wavelet filters. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 36(6), 1273–1282.
Liu, G., Yang, J., & Li, Z. (2015). Content-based image retrieval using computational visual attention model. Pattern Recognition, 48(8), 2554–2566.
Liu, L., Lao, S., Fieguth, P. W., Guo, Y., Wang, X., & Pietikäinen, M. (2016). Median robust extended local binary pattern for texture classification. IEEE Transactions on Image Processing, 25(3), 1368–1381.
Nene, S. A., Nayar, S. K., & Murase, H. (1996). Columbia object image library (COIL-100). Technical Report CUCS-006-96, Columbia University.
Muqeet, M. A., & Holambe, R. S. (2018). Local binary patterns based on directional wavelet transform for expression and pose-invariant face recognition. Applied Computing and Informatics. https://doi.org/10.1016/j.aci.2017.11.002.
Murala, S., & Jonathan Wu, Q. M. (2014). Expert content-based image retrieval system using robust local patterns. The Journal of Visual Communication and Image Representation, 25, 1324–1334.
Ojala, T., Pietikäinen, M., & Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971–987.
Oliva, A., & Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal Computer Vision, 42, 145–175.
Pavithra, L. K., & Sree Sharmila, T. (2017). Retrieval of homogeneous images using appropriate color space selection. In International conference on computational intelligence in data mining, (pp. 739-747).
Qian, X., Hua, X.-S., Chen, P., & Ke, L. (2011). PLBP: An effective local binary patterns texture descriptor with pyramid representation. Pattern Recognition, 44, 2502–2515.
Riaz, F., Hassan, A., Rehman, S., & Qamar, U. (2013). Texture classification using rotation- and scale-invariant gabor texture features. IEEE Signal Processing Letters, 20(6), 607–610.
Safia, A., & He, D. (2013). New brodatz-based image databases for grayscale color and multiband texture analysis. ISRN Machine Vision, 2013, 876386.
Shakoor, M. H., & Boostani, R. (2018). Radial mean local binary pattern for noisy texture classification. Multimedia Tools and Applications, 77, 21481–21508. https://doi.org/10.1007/s11042-017-5440-0.
Srivastava, P., & Khare, A. (2017). Integration of wavelet transform, local binary patterns and moments for content-based image retrieval. The Journal of Visual Communication and Image Representation, 42, 78–103.
Subrahmanyam, S., Maheshwari, R. P., & Balasubramanian, R. (2012). Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Transactions on Image Processing, 21(5), 2874–2886.
Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19(6), 1635–1650.
Verma, M., & Raman, B. (2015). Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval. The Journal of Visual Communication and Image Representation, 32, 224–236.
Verma, M., Raman, B., & Murala, S. (2015). Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing, 165, 255–269.
Yadav, A. R., Anand, R. S., Dewal, M. L., & Gupta, S. (2015). Hardwood species classification with DWT based hybrid texture feature extraction techniques. Sadhana, 40(8), 2287–2312.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pavithra, L.K., Sree Sharmila, T. A new multi-level radial difference encoded pattern for image classification and retrieval. Multidim Syst Sign Process 31, 1411–1433 (2020). https://doi.org/10.1007/s11045-020-00713-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11045-020-00713-4