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This paper investigates different methods of representing shape and texture in content-based image retrieval. We have combined five features set in our work and these are trained and classified with SVM (support vector machine) classifier which makes use of machine learning technology. We combined histogram features, texture features (GLCM features), wavelet features, Gabor features, and statistical...
Image retrieval is an active research area for the last two decades. This area is gaining more importance as the multimedia content over the internet is increasing. Color Texture and shape are the low level image descriptor in Content Based Image Retrieval. These low level image descriptors are used for image representation and retrieval in CBIR. This paper presents a Content Base Image Retrieval...
Flower image retrieval is a significant and challenging problem in content-based image retrieval. We had systematic and overall researches on flower images, including repetitive images filtering, regional segmentation, feature extraction and image retrieval based on SVM, etc. Firstly, in order to ensure retrieval results, we propose a repetitive images detection algorithm based on Canny edge to filter...
Teaching resources are usually massive, complex, hybrid and distributed. In order to search and utilize images from teaching resources with high efficiency, this paper introduced how to use CBIR (Content-based Image Retrieval) to search images from teaching resources, and design a image retrieval system for Teaching resources, it emphasizes on the analysis of relevance feedback algorithm based on...
The influence of the feature vector (FV) content on the CBIR (content-based image retrieval) system efficiency was considered. By using two different FVs and applying three different learning methods, it was shown that the efficiency of retrieving depends on both the FV content and the learning method, independently.
In this paper, an heterogeneous image recognition system based on content description and classification is proposed. In this system and for an heterogeneous image database several features extraction methods are used and applied to better describes the images content. The features relevance is tested and improved through support vectors machines (SVMs) classifier of the consequent images index database...
Digital animation is a widely used digital media on Internet to convey information. However, many animations nowadays are usually advertisements and contain only junk information. In order to detect and filter such information, a feature extraction, analysis and classification method for animation content understanding is proposed. A feature set composed of the traditional image/video features and...
This paper proposes an automatic semantic video content indexing and retrieval system based on fusing various low level visual and shape descriptors. Extracted features from region and sub-image blocks segmentation of video shots key-frames are described via IVSM signature (Image Vector Space Model) in order to have a compact and efficient description of the content. Static feature fusion based on...
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