72,048 research outputs found
AN OPTIMIZED FEATURE EXTRACTION TECHNIQUE FOR CONTENT BASED IMAGE RETRIEVAL
Content-based image retrieval (CBIR) is an active research area with the development of multimedia technologies and has become a source of exact and fast retrieval. The aim of CBIR is to search and retrieve images from a large database and find out the best match for the given query. Accuracy and efficiency for high dimensional datasets with enormous number of samples is a challenging arena. In this paper, Content Based Image Retrieval using various features such as color, shape, texture is made and a comparison is made among them. The performance of the retrieval system is evaluated depending upon the features extracted from an image. The performance was evaluated using precision and recall rates. Haralick texture features were analyzed at 0 o, 45 o, 90 o, 180 o using gray level co-occurrence matrix. Color feature extraction was done using color moments. Structured features and multiple feature fusion are two main technologies to ensure the retrieval accuracy in the system. GIST is considered as one of the main structured features. It was experimentally observed that combination of these techniques yielded superior performance than individual features. The results for the most efficient combination of techniques have also been presented and optimized for each class of query
Comparative analysis of image search algorithm using average RGB, local color histogram, global color histogram and color moment HSV
Image retrieval forms a major problem when a large database is considered. Content
Base Image Retrieval (CBIR) makes use of the available visual features of the image
and helps in retrieving similar image as that of the query image. In the CBIR
method, each image stored in the database has its features extracted and compared to
the features of the query image. Thus, it involves two processes, feature extraction
and feature matching. In this thesis, four techniques have been used, which are the
Average of Red, Green and Blue Color Channels (Average RGB), Local Color
Histogram (LCH), Global Color Histogram (GCH) and Color Moment of Hue,
Saturation and Brightness Value (HSV) to retrieve relevant images based on colour.
These techniques are applied on the collection of three images chosen randomly
from each class of Wang images database. The performance of each technique has
been individually evaluated, in terms of Execution Time, Precision, Recall,
Accuracy, Redundancy Factor and Fall Rate. The results were then analysed and
compared. The comparison was shown in bar graphs that the Average RGB
technique has the best performance, where it obtained high accuracy. As a
conclusion to the report, this comparative study contributes to the image searching
field, by measuring the performance for several CBIR techniques using more
commonly used parameters
Enhancing performance of Image Retrieval Systems using Dual Tree Complex Wavelet Transform and Support Vector Machines
This paper presents a novel image retrieval system (SVMBIR) based on dual tree complex wavelet transform (CWT) and support vector machines (SVM). We have shown that how one can improve the performance of image retrieval systems by assuming two attributes. Firstly, images that user needs through query image are similar to a group of images with same conception. Secondly, there exists non-linear relationship between feature vectors of different images and can be exploited very efficiently with the use of support vector machines. At first level, for low level feature extraction we have used dual tree complex wavelet transform because recently it is proven to be one of the best for both texture and color based features. At second level to extract semantic concepts, we grouped images of typical classes with the use of one against all support vector machines. We have also shown how one can use a correlation based distance metric for comparison of SVM distance vectors. The experimental results on standard texture and color datasets show that the proposed approach has superior retrieval performance over the existing linear feature combining techniques
AUTOMATED FEATURE EXTRACTION AND CONTENT-BASED RETRIEVAL OFPATHOLOGY MICROSCOPIC IMAGES USING K-MEANS CLUSTERING AND CODE RUN-LENGTH PROBABILITY DISTRIBUTION
The dissertation starts with an extensive literature survey on the current issues in content-based image retrieval (CBIR) research, the state-of-the-art theories, methodologies, and implementations, covering topics such as general information retrieval theories, imaging, image feature identification and extraction, feature indexing and multimedia database search, user-system interaction, relevance feedback, and performance evaluation. A general CBIR framework has been proposed with three layers: image document space, feature space, and concept space. The framework emphasizes that while the projection from the image document space to the feature space is algorithmic and unrestricted, the connection between the feature space and the concept space is based on statistics instead of semantics. The scheme favors image features that do not rely on excessive assumptions about image contentAs an attempt to design a new CBIR methodology following the above framework, k-means clustering color quantization is applied to pathology microscopic images, followed by code run-length probability distribution feature extraction. Kulback-Liebler divergence is used as distance measure for feature comparison. For content-based retrieval, the distance between two images is defined as a function of all individual features. The process is highly automated and the system is capable of working effectively across different tissues without human interference. Possible improvements and future directions have been discussed
Plant image retrieval using color, shape and texture features
We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques
and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered
Content-based image retrieval for fish based on extended zernike moments-local directional pattern-hue color space
Scholars have been fascinated in the areas of the
description and representation of fish species images so the
Content-based Image Retrieval is adopted. Proposals have been made to use various techniques like the fusion of Zernike
Moments (ZM) and Local Directional Pattern (LDP) to obtain
good image representation and description results for feature
extraction. To elaborate, ZM is characteristically rotation
invariant and it is very robust in the extraction of the global
shape feature and the LDP serves as the texture and local shape feature extractors. Nevertheless, extant studies on ZM-LDP fusion are only adopted for gray-level. The role of color is
substantial for the fish. The proposal is that the ZM-LDP method is improved so that it can bring out the color features for the fishdomain effectively. By computing the LDP on the Hue plane of the HSV color space of the image, the color information is obtained. Improved ZM-LDP fusion to be able to obtain color information (Extended Zernike Moments-Local Directional Pattern-Hue Color Space) is experimented on Fish4Knowledge (natural image) dataset consists of 27370 images and able to achieve Mean Average Precision of 77.62%. Based on the experimental results, it is shown that the proposed method has successfully achieved higher accuracy compared to other comparable methods. A statistical comparison based on the Twotailed paired t-test was carried out and has proven that the retrieval performance of the proposed method is improved
Packing and Padding: Coupled Multi-index for Accurate Image Retrieval
In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low
discriminative power, so false positive matches occur prevalently. Apart from
the information loss during quantization, another cause is that the SIFT
feature only describes the local gradient distribution. To address this
problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform
feature fusion at indexing level. Basically, complementary features are coupled
into a multi-dimensional inverted index. Each dimension of c-MI corresponds to
one kind of feature, and the retrieval process votes for images similar in both
SIFT and other feature spaces. Specifically, we exploit the fusion of local
color feature into c-MI. While the precision of visual match is greatly
enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation
of SIFT and color features significantly reduces the impact of false positive
matches.
Extensive experiments on several benchmark datasets demonstrate that c-MI
improves the retrieval accuracy significantly, while consuming only half of the
query time compared to the baseline. Importantly, we show that c-MI is well
complementary to many prior techniques. Assembling these methods, we have
obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench
datasets, respectively, which compare favorably with the state-of-the-arts.Comment: 8 pages, 7 figures, 6 tables. Accepted to CVPR 201
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