H.5. Image Processing and Computer Vision
S. Asadi Amiri; Z. Mohammadpoory; M. Nasrolahzadeh
Abstract
Content based image retrieval (CBIR) systems compare a query image with images in a dataset to find similar images to a query image. In this paper a novel and efficient CBIR system is proposed using color and texture features. The color features are represented by color moments and color histograms of ...
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Content based image retrieval (CBIR) systems compare a query image with images in a dataset to find similar images to a query image. In this paper a novel and efficient CBIR system is proposed using color and texture features. The color features are represented by color moments and color histograms of RGB and HSV color spaces and texture features are represented by localized Discrete Cosine Transform (DCT) and localized Gray level co-occurrence matrix and local binary patterns (LBP). The DCT coefficients and Gray level co-occurrence matrix of the blocks are examined for assessing the block details. Also, LBP is used for rotation invariant texture information of the image. After feature extraction, Shannon entropy criterion is used to reduce inefficient features. Finally, an improved version of Canberra distance is employed to compare similarity of feature vectors. Experimental analysis is carried out using precision and recall on Corel-5K and Corel-10K datasets. Results demonstrate that the proposed method can efficiently improve the precision and recall and outperforms the most existing methods.s the most existing methods.
M. Azimi hemat; F. Shamsezat Ezat; M. Kuchaki Rafsanjani
Abstract
In content-based image retrieval (CBIR), the visual features of the database images are extracted, and the visual features database is assessed to find the images closest to the query image. Increasing the efficiency and decreasing both the time and storage space of indexed images is the priority in ...
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In content-based image retrieval (CBIR), the visual features of the database images are extracted, and the visual features database is assessed to find the images closest to the query image. Increasing the efficiency and decreasing both the time and storage space of indexed images is the priority in developing image retrieval systems. In this research, an efficient system is proposed for image retrieval by applying fuzzy techniques, which are advantageous in increasing the efficiency and decreasing the length of the feature vector and storage space. The effect of increasing the considered content features' count is assessed to enhance image retrieval efficiency. The fuzzy features consist of color, statistical information related to the spatial dependency of the pixels on each other, and the position of image edges. These features are indexed in fuzzy vector format 16, 3, and 16 lengths. The extracted vectors are compared through the fuzzy similarity measures, where the most similar images are retrieved. To evaluate the proposed systems' performance, this system and three other non-fuzzy systems where fewer features are of concern were implemented. These four systems are tested on a database containing 1000 images, and the results indicate improvement in the retrieval precision and storage space.
F. Jafarinejad; R. Farzbood
Abstract
Image retrieval is a basic task in many content-based image systems. Achieving high precision, while maintaining computation time is very important in relevance feedback-based image retrieval systems. This paper establishes an analogy between this and the task of image classification. Therefore, in the ...
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Image retrieval is a basic task in many content-based image systems. Achieving high precision, while maintaining computation time is very important in relevance feedback-based image retrieval systems. This paper establishes an analogy between this and the task of image classification. Therefore, in the image retrieval problem, we will obtain an optimized decision surface that separates dataset images into two categories of relevant/irrelevant images corresponding to the query image. This problem will be viewed and solved as an optimization problem using particle optimization algorithm. Although the particle swarm optimization (PSO) algorithm is widely used in the field of image retrieval, no one use it for directly feature weighting. Information extracted from user feedbacks will guide particles in order to find the optimal weights of various features of images (Color-, shape- or texture-based features). Fusion of these very non-homogenous features need a feature weighting algorithm that will take place by the help of PSO algorithm. Accordingly, an innovative fitness function is proposed to evaluate each particle’s position. Experimental results on Wang dataset and Corel-10k indicate that average precision of the proposed method is higher than other semi-automatic and automatic approaches. Moreover, the proposed method suggest a reduction in the computational complexity in comparison to other PSO-based image retrieval methods.
