In CBIR (Content-Based Image Retrieval), visual features such as shape, color and texture are extracted to characterize images. Each of the features is represented using one or more feature descriptors. During the retrieval, features and descriptors of the query are compared to those of the images in the database in order to rank each indexed image according to its distance to the query. In bio...

A quality metric based on a classification process is introduced. The main idea of the proposed method is to avoid the error pooling step of many factors (in frequential and spatial domain) commonly applied to obtain a final quality score. A classification process based on the Support Vector Machine method is designed to obtain the final quality class with respect to the standard quality scale ...

Classification of different types of rice is carried out in this study using metaheuristic classification approaches.13 different rice samples are considered. Images of milled rice are acquired using a computer vision system. Feature Extraction methods are used to extract fifty seven features including five shape and size features, forty eight color features and four texture features from color...

Image retrieval based on color, texture and shape is a wide area of research scope. In this paper we present a framework for combining all the three i.e. color, texture and shape information, and achieve higher retrieval efficiency. The image and its complement are partitioned into non-overlapping tiles of equal size. The features drawn from conditional co-occurrence histograms between the imag...

Abstract: In CBIR (Content-Based Image Retrieval), visual features such as shape, color and texture are extracted to characterize images. Each of the features is represented using one or more feature descriptors. During the retrieval, features and descriptors of the query are compared to those of the images in the database in order to rank each indexed image according to its distance to the que...

While histogram or global feature approaches are powerful methods to encode image information for retrieval purposes, they suffer from a complete lack of spatial information. One possibility to reduce this shortcoming is to store feature vectors of subregions. However, this procedure increases the size of the index vector. The paper suggests to store only the differences of the features between...

In this paper, we propose a generic and e cient contentbased image retrieval architecture. We compute "real" interimage distances for an initial subset of the images that are to be stored into an image database. For computing real interimage distances we use image content based on a low level feature. High-level image feature vectors are computed from the real interimage distances in such a way...

In the present paper, an expert system for automatic recognition of different plant species through their leaf images is investigated by employing the ant colony optimization (ACO) as a feature decision-making algorithm. The ACO algorithm is employed to investigate inside the feature search space in order to obtain the best discriminant features for the recognition of individual species. In ord...