Content based Image Retrieval using Color Histogram (original) (raw)
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New Approach to Image Retrieval Based on Color Histogram
Lecture Notes in Computer Science, 2013
Nowadays a lot of information in the form of digital content is easily accessible but finding the relevant image is a big problem. This is where the Content Based Image Retrieval (CBIR) comes in to solve the image retrieval dilemma. But a CBIR system faces certain problems such as a strong signature development. Also, one of the major challenges of CBIR is to bridge the gap between the low level features and high level semantics. Previously, several researchers have proposed to improve the performance of a CBIR system but they have only answered image retrieval problem to an extent. In this paper, we propose a new CBIR signature that uses color color histogram. The results of the proposed method are compared previous method from the literature. The results of the proposed system demonstrates high accuracy rate than the previous systems in the simulations. The proposed system has significant performance.
IAEME PUBLICATION, 2014
Content Based Image Retrieval (CBIR) is the technique which uses visual contents to search images from large database. Image retrieval is achieved according to the similarity of image features. Color and texture is the most important visual features. CBIR systems focused on using low-level features like color, texture and shape for image representation. The color and texture features can be extracted using global color histogram. This paper measures the performance of color and textual statistical features of an image in retrieving the similar images from the data set. The performance is measured by implementing a single CBIR system in two levels. The first level uses color features for image retrieval. The second level uses the texture features for similar image extraction. Euclidean distance is used as the distance measure of two images. It is found that the system which uses texture features retrieves the most similar images from the data set.
An Introduction of Content Based Image Retrieval Process
Image retrieval plays an important role in many areas like fashion, Engineering, Fashion, Medical, advertisement etc. As the process become increasingly powerful and memories become increasingly cheaper, the deployment of large image database for a variety of applications. It has now become realisable. Content Based image retrieval is method of extracting the similar image or matched image from the image database. It has become popular for getting image from the large image database. By using low level features like colour, texture, shape the retrieval become more efficient. There are many research algorithm developed for CBIR. In this paper we have used two retrieval process query by colour and query by texture. Colour features contain colour histogram in RGB colour space and texture features involve the invariant histogram and mean and standard deviation to retrieve the image. It is observed from the experiment that query by texture is more effective than colour for retrieving images. Precision and Recall provides the performance measurement.
Content Based Image Retrieval Using Local Color Histogram
International Journal of Engineering Research, 2014
This paper proposes a technique to retrieve images based on color feature using local histogram. The image is divided into nine sub blocks of equal size. The color of each sub-block is extracted by quantifying the HSV color space into 12x6x6 histogram. In this retrieval system Euclidean distance and City block distance are used to measure similarity of images. This algorithm is tested by using Corel image database. The performance of retrieval system is measured in terms of its recall and precision. The effectiveness of retrieval system is also measured based on AVRR (Average Rank of Relevant Images) and IAVRR (Ideal Average Rank of Relevant Images) which is proposed by Faloutsos. The experimental results show that the retrieval system has a good performance and the evaluation results of city block has achieved higher retrieval performance than the evaluation results of the Euclidean distance.
A Survey on Content Based Image Retrieval System
As the number of digital images increase a general TBIR system will not retrieve a large number of images depend on text. Content-based image retrieval has become one of the most active research areas in the past few years. This paper will give an overview of retrieving images from a large database. CBIR depends on various features like Low level or High level. The low level features include color, texture and shape. They are the visual feature to represent the image. The high level feature describes the concept of human brain. A Single feature can represent only a part of the image property. So multiple features are used to increase the efficiency of the image retrieval process. This paper will provide a survey on used color histogram, color mean, color structure descriptor and texture for feature extraction and how they are retrieved depending on their Euclidean distance.
Colour Features Extraction Techniques and Approaches for Content-Based Image Retrieval (CBIR) System
Journal of Materials Science and Chemical Engineering, 2021
An image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the large database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and colour similarity. Retrieving images based on the contents which are colour, texture, and shape is called content-based image retrieval (CBIR). This paper discusses and describes about the colour features technique for image retrieval systems. Several colour features technique and algorithms produced by the previous researcher are used to calculate the similarity between extracted features. This paper also describes about the specific technique about the colour basis features and combined features (hybrid techniques) between colour and shape features.
Query by Image Content Using Color Histogram Techniques
2013
The extensive digitization of images, diagrams and paintings, traditional keyword based search has been found to be inefficient for retrieval of the required data. Content-Based Image Retrieval (CBIR) system responds to image queries as input and relies on image content, using techniques from computer vision and image processing to interpret and understand it, while using techniques from information retrieval and databases to rapidly locate and retrieve images suiting an input query. In this paper, we aim to evaluate and present the Content Based Image Retrieval (CBIR) system. Various methods have been proposed for CBIR using image low level image features like color with color histogram, color layout, texture and shape. This paper CBIR is proposed with color histogram feature. To compare the histogram and find the errors for that histogram if the error is beyond the threshold then not retrieval of images otherwise it is retrieval of images. After retrieval the precision and recall are calculated for each query image and retrieve the best output.
Content Based Image Retrieval Using Color Feature
2013
The purpose of this paper is to describe the problem of designing a Content Based Image Retrieval, CBIR system. Using color feature. Due to the enormous increase in image database sizes, as well as its vast deployment in various applications, the need for CBIR development arose. In CBIR systems, image processing techniques are used to extract visual features such as color, texture and shape from images. Therefore, images are represented as a vector of extracted visual features instead of just pure textual annotations. Color, which represents physical quantities of objects, is an important attribute for image matching and retrieval. Many publications focus on color indexing techniques based on global color distributions. Color correlogram and color coherence [6] vector can combine the spatial correlation of color regions as well as the global distribution of local spatial correlation of colors. These techniques perform better than traditional color histograms when used for content-based image retrieval. However, they require very expensive computation
Use of Picture Information Measure using Color Features for Image Retrieval in CBIR
IJCSMC, 2021
Content-Based Image Retrieval systems backups the image retrieval process using the primary characteristics of image like colour, shape, texture and spatial locations clubbed with the semantic approaches for better efficiency and performance. Various information measures have been proposed in order to increase the level of Retrieval. A method of picture information measures based upon the concept of the minimum number of gray level changes to convert a picture into one with a desired histogram is presented. In search of finding a new perspective an integrated approach of Picture Information Measure (PIM) employed with the primitive visual feature color. The retrieval results obtained by applying color histogram (CH) on PIM (PIM of Red Green and Blue and there integrated variation) + Color Moment to a 1000 image database demonstrated significant improvement in retrieval effectively.