A smart content-based image retrieval system based on color and texture feature

Digital image libraries and other multimedia databases have been dramatically expanded in recent years. In order to effectively and precisely retrieve the desired images from a large image database, the development of a content-based image retrieval (CBIR) system has become an important research issue. However, most of the proposed approaches emphasize on finding the best representation for different image features. Furthermore, very few of the representative works well consider the user's subjectivity and preferences in the retrieval process. In this paper, a user-oriented mechanism for CBIR method based on an interactive genetic algorithm (IGA) is proposed. Color attributes like the mean value, the standard deviation, and the image bitmap of a color image are used as the features for retrieval. In addition, the entropy based on the gray level co-occurrence matrix and the edge histogram of an image are also considered as the texture features. Furthermore, to reduce the gap between the retrieval results and the users' expectation, the IGA is employed to help the users identify the images that are most satisfied to the users' need. Experimental results and comparisons demonstrate the feasibility of the proposed approach.

A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm

Semantic content-based image retrieval: A comprehensive study ☆

The increased need of content based image retrieval technique can be found in a number of different domains such as Data Mining, Education, Medical Imaging, Crime Prevention, Weather forecasting, Remote Sensing and Management of Earth Resources. This paper presents the content based image retrieval, using features like texture and color, called WBCHIR (Wavelet Based Color Histogram Image Retrieval).The texture and color features are extracted through wavelet transformation and color histogram and the combination of these features is robust to scaling and translation of objects in an image. The proposed system has demonstrated a promising and faster retrieval method on a WANG image database containing 1000 general-purpose color images. The performance has been evaluated by comparing with the existing systems in the literature.

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Content Based Image Retrieval using Color and Texture

This paper presents a proposed model for content-based image retrieval (CBIR) which depends only on extracting the most relevant features according to a feature selection technique. The suggested feature selection technique aims at selecting the optimal features that not only maximize the detection rate but also simplify the computation of the image retrieval process. The proposed model is divided into three main techniques, the first one is concerned with the features extraction from images database, the second is performing feature discrimination, and the third is concerned with the feature selection from the original ones. As for the first technique, the 3D color histogram and the Gabor filter algorithm are used to extract the color and texture features respectively. While the second technique depends on a genetic algorithm (GA) for replacing numerical features with nominal features that represent intervals of numerical domains with discrete values. The GA is utilized in this technique to obtain the optimal boundaries of these intervals, and consequently to reduce the complexity in feature space. In the third technique, the feature selection performs two successive functions which are called preliminary and deeply reduction for extracting the most relevant features from the original features set. Indeed, the main contribution of the proposed model is providing a precise image retrieval in a short time.

N. Jhanwar

Papers

Content based image retrieval using motif cooccurrence matrix