Abstract-In this paper, a novel algorithm which integrates the RGB color histogram and texture features for content based image retrieval. A new set of twodimensional (2-D) M-band dual tree complex wavelet transform (M_band_DT_CWT) and rotated M_band_DT_CWT are designed to improve the texture retrieval performance. Unlike the standard dual tree complex wavelet transform (DT_CWT), which gives a logarithmic frequency resolution, the M-band decomposition gives a mixture of a logarithmic and linear frequency resolution. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for image retrieval using M_band_DT_CWT and rotated M_band_DT_CWT (M_band_DT_RCWT) by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval performance, two texture databases are used. Further, it is mentioned that the databases used are Brodatz gray scale database and MIT VisTex Color database. The retrieval efficiency and accuracy using proposed features is found to be superior to other existing methods.
Diabetic retinopathy is a disorder induced by long-term diabetes that can result in total blindness if not addressed. As a result, early detection of diabetic retinopathy is critical, as is the medical treatment to prevent its adverse effects. Manual ophthalmologist detection takes longer and produces considerable discomfort during examination. Machine learning has recently become one of the most popular strategies for improving performance in a variety of sectors, including medical picture analysis and classification. As a result, an automated system aids in the early detection of diabetic retinopathy. Using a combination of neural networks, this research offers the extraction of exudates, haemorrhages, and micro-aneurysms and classification by machine learning.
This paper presents the integration of color histogram and DBC co-occurrence matrix for content based image retrieval. The exit DBC collect the directional edges which are calculated by applying the first-order derivatives in 0º , 45º , 90º and 135º directions. The feature vector length of DBC for a particular direction is 512 which are more for image retrieval. To avoid this problem, we collect the directional edges by excluding the center pixel and further applied the rotation invariant property. Further, we calculated the cooccurrence matrix to form the feature vector. Finally, the HSV color histogram and the DBC co-occurrence matrix are integrated to form the feature database. The retrieval results of the proposed method have been tested by conducting three experiments on Brodatz, MIT VisTex texture databases and Corel-1000 natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, DBC and other transform domain features.
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