Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns

Today, large amount of data are stored in image format. Content based image retrieval from bulk databases has become an interesting research topic in last decade. Most of the recent approaches use joint of texture and color information. In most cases, the color and texture features are concatenated together and equal importance is given to each one. The human visual system, usually pays more attention to the textural properties of objects to recognize. In this paper a new approach is proposed for content based image retrieval based on weighted combination of color and texture features. Firstly, to achieve discriminant features, texture features are extracted using modified local binary patterns (MLBP) and local neighborhood differences patterns (LNDP) and filtered gray level co-occurrence matrix (GLCM). Also, quantization color histogram is used to extract color features. Next, the similarity matching is performed based on canbera distance in color and texture features separatly. Finally, a weighted decision is performed to retrieve most similar database images to the user query. The performance of the proposed approach is evaluated on Corel 1 K and Corel 10k datasets. Results show that proposed approach provide better performance than state-of-the-art methods in terms of precision and recall rate.