Abstract
In CBIR (content-based image retrieval) features are extracted based on color, texture, and shape. There are many factors affecting the accuracy (precision) of retrieval such as number of features, type of features (local or global), color model, and distance measure. In this paper, a two phases approach to retrieve similar images from data set based on color and texture is proposed. In the first phase, global color histogram is utilized with HSV (hue, saturation, and value) color model and an automatic cropping technique is proposed to accelerate the process of features extraction and enhances the accuracy of retrieval. Joint histogram and GLCM (gray-level co-occurrence matric) are deployed in phase two. In this phase, color features and texture features are combined to enhance the accuracy of retrieval. Finally, a new way of using K-means as clustering algorithm is proposed to classify and retrieve images. Two experiments are conducted using WANG database. WANG database consists of 10 different classes each with 100 images. Results of comparing the proposed approach with the most relevant approaches are promising.
Highlights
Visual cues rather than textual description of images is used in CBIR to extract features automatically from query image and image database and the most similar images are ranked and retrieved
Color and texture features are considered as the most frequently used in CBIR that because it is easy to extract features based on these visual contents
The second method relies on dividing images to equal blocks or regions such as [1, 6] and utilizes local color histogram for each region to extract color features
Summary
Visual cues rather than textual description of images is used in CBIR to extract features automatically from query image and image database and the most similar images are ranked and retrieved. The second method relies on dividing images to equal blocks or regions such as [1, 6] and utilizes local color histogram for each region to extract color features. These approaches provides better information related to spatial distribution of colors. In the second phase images are sliced to three different regions and for each region the most significant features of color and texture based on joint histogram and gray level co-occurrence are calculated.
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