A Content Based Image Retrieval using Color and Texture Features

In content based image retrieval(CBIR), the searching and retrieving of similar kinds of digital images from an image database are realized on the visual features of a given query image. The efficiency and accuracy of any CBIR scheme depends on the extracted significant visual features of the digital images. This paper considered a CBIR scheme based on the proficient combination of extracted color and texture visual features. The visual features are extracted from the enhanced HSV color image after enhancing the RGB color image using Laplacian filter. In the presented work, the color feature is extracted from the quantized histograms of Hue (H) and Saturation (S) components while texture feature is extracted from computed gray level co-occurrence matrices (GLCMs) of each sub image of discrete wavelet transform (DWT) of Value (V) component of HSV color image. The extracted color and texture visual features are combined together after normalizing them individually. The proposed CBIR scheme is evaluated on standard Corel image database and observed that the combined feature vector produces the satisfactory results in terms of performance evaluation metrics i.e. precision, recall and F-score. The experimental results are also showed that the proposed CBIR scheme outperforms as compare to the some other existing schemes.