Image Retrieval using OpenCV, Python
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Classification of Very High Resolution Optical Images Using Wavelet and LBP-Based Features for Image Retrieval Application
Platform : Python
Delivery Duration : 3-4 working Days
100 in stock
The project proposes the image retrieval technique based on GLCM and LBP features. The main target of CBIR is to get accurate results with lower computational time. The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and web image classification and searching. Content-based Image Retrieval (CBIR) technology overcomes the defects of traditional text-based image retrieval technology, such as heavy workload and strong subjectivity Local Binary Pattern (LBP) is an effective method for texture analysis, being appreciated for accuracy and computing power. The LBP operator is invariant to illumination and contrast changes in the image. Intensity Histogram is a simple method used to compare images with the advantage of being insensitive to small changes of camera position. It makes full use of image content features (colour, texture, shape, etc.), which are analysed and extracted automatically by computer to achieve the effective retrieval Using a single feature for image retrieval cannot be a good solution for the accuracy and efficiency… Using Euclidean distance, similarity between queried image and the candidate images are calculated. Future work will be made to add more features that are famous in CBIR which are texture, colour, and shape features in order to get better results.
- Text based retrieval
- Image retrieval using single feature
- Less accuracy in Performance
- It is not suitable for all Images
Content Based Image Retrieval (CBIR)
LBP Features with Wavelet
(LBP) operator is proposed and an associated completed LBP (CLBP) scheme is developed for texture classification. A local region is represented by its center pixel and a wavelet transform. The center pixels represent the image gray level and they are converted into a binary code, namely CLBP-Center (CLBP_C), by global thresholding. Dwt decomposes the image local differences into two complementary components: the signs and the magnitudes, and two operators, namely CLBP-Sign (CLBP_S) and CLBP-Magnitude (CLBP_M), are proposed to code them. The traditional LBP is equivalent to the CLBP_S part of CLBP, and they show that CLBP_S preserves more information of the local structure than CLBP_M, which explains why the simple LBP operator can extract the texture features reasonably well. By combining CLBP_S, CLBP_M, and CLBP_C features into joint or hybrid distributions, significant improvement can be made for rotation invariant texture classification.
It is the most widely used low level features because it is salient and powerful in visual cue.
A histogram is nothing but a graph that represents all the colors and the level of their occurrence in an image irrespective of the type of the image. Few basic properties about an image can be obtained from using a Histogram. It can be used to set a threshold for screening the images. The shape and the concentration of the colors in the histogram will be the same for similar objects even though they are of different colors. Identifying objects in a grey scale image is the easiest one as the histogram is almost similar as the objects have the same colors for same objects. In order for identifying the objects in the images or generating the histogram the system has to obtain the array values.
Gray Level Co-occurrence Matrix (GLCM)
It is useful since it can contain three major elements, texture information, histogram information and edge information.
The GLCM is a well-established statistical device for extracting second order texture information from images. A GLCM is a matrix where the number of rows and columns is equal to the number of distinct gray levels or pixel values in the image of that surface. GLCM is a matrix that describes the frequency of one gray level appearing in a specified spatial linear relationship with another gray level within the area of investigation. Given an image, each with an intensity, the GLCM is a tabulation of how often different combinations of gray levels co-occur in an image or image section.
- Accurate results compared to Histogram with Wavelet+GLCM and LBP for texture features
- High retrieval accuracy
- Searching Applications
- Web Applications
- Medical field
Thus the Local Binary Pattern and Wavelets are used for the high resolution image retrieval, by this retrieval of image is accuracy and we can expect the more relevant image retrieval.
 D. L. Rubin, H. Greenspan, and J. F. Brinkley, “Biomedical Imaging Informatics,” Biomedical Informatics, pp. 285-327, 2014. Springer London.
 M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker, “Query by image and video content: The QBIC system,” Computer, vol. 28, no. 9, pp. 23-32, 1995.
 A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-based image retrieval at the end of the early years,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349–1380, Dec. 2000.
 Y. Liu, D. Zhang, G. Lu, and W. Y. Ma, “A survey of content-based image retrieval with high-level semantics,” Pattern Recog., vol. 40, pp. 262–282, 2007.
 H. Muller, A. Rosset, J. P. Vallee, and A. Geisbuhler, “Comparing feature sets for content-based image retrieval in a medical case database,” in Proc. SPIE Med. Imag., PACS Imag. Inf., 2004, pp. 99 – 109.
 L. Zheng, A.W. Wetzel, J. Gilbertson, and M.J. Becich, “Design and analysis of a content-based pathology image retrieval system,” IEEE Trans. Inf. Tech. Biomed., vol. 7, no. 4, pp. 249-255, 2003.
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