Fast Image Retrieval Prototype using Color Descriptor

2016 ◽  
Vol 1 (22) ◽  
pp. 745-758
Author(s):  
Bushra Abdul-Kareem Abdul-Azeez
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Image Database ◽  
Query Image ◽  
Color Feature ◽  
Retrieval Time ◽  
Dominant Color ◽  
Color Descriptor

In recent years, image retrieval prototypes become important and increased noticeably. Color feature is one of the most significant features to represent image. In this paper, we use a Dominant Color (DC) feature to represent images where each image represented by 8-DCs as maximum. Based on DCs values, image database is indexed using 3-D RGB partitioning color space. This is to reduce searching process where once a query image is given to the prototype; it will not search the whole database. Proposed technique will identify the partition and search the image within this partition only. According to the proposed method, extensive experiments were conducted on Corel databases. As a result, the retrieval time is reduced significantly without degradation to precision of retrieval.

An Effective Similarity Measurement Algorithm for Dominant Color Feature Matching in Image Retrieval

2012 ◽  
Vol 182-183 ◽  
pp. 1169-1173
Author(s):  
Li Fang Yang ◽  
Xiang Lin Huang ◽  
Rui Lv ◽  
Hui Lv
Keyword(s):  
Image Retrieval ◽  
Feature Matching ◽  
Color Space ◽  
Similarity Measurement ◽  
Average Precision ◽  
Image Region ◽  
Color Feature ◽  
Color Distribution ◽  
Dominant Color ◽  
Measurement Algorithm

For the reason that dominant colors can characterize color information of image region and can represent the image using fewer dimensions, it is one of the widely used color features in image retrieval. We extract the dominant color feature in HSV color space, and combine it with color distribution information. In this paper, a new similarity measurement algorithm based on block distance is proposed for dominant color matching. Our proposed algorithm not only takes the distance between dominant colors into account, but also the difference of the percentage of dominant colors. The average precision of our algorithm improves about 5% and about 14% respectively compared with block distance and Euclidean distance. Although the average precision of our algorithm is almost equal to quadratic form distance, the computation cost of our algorithm is obviously less than it.

Image Retrieval Based on MPEG-7 Dominant Color Descriptor

2008 ◽  
Author(s):  
Hong Shao ◽  
Yueshu Wu ◽  
Wencheng Cui ◽  
Jinxia Zhang
Keyword(s):  
Image Retrieval ◽  
Dominant Color ◽  
Color Descriptor ◽  
Dominant Color Descriptor

Semantic Mapping of Color Feature and its Application in Content Based Image Retrieval

2012 ◽  
Vol 263-266 ◽  
pp. 2488-2492
Author(s):  
You Ping Zhong ◽  
Biao Peng ◽  
Jun Li ◽  
Chong Yang Zhang
Keyword(s):  
Image Retrieval ◽  
Mapping Method ◽  
Content Based Image Retrieval ◽  
Semantic Mapping ◽  
Semantic Retrieval ◽  
Semantic Description ◽  
Color Feature ◽  
Color Features ◽  
Color Descriptor ◽  
Dominant Color Descriptor

To support content based image retrieval, MPEG-7 is developed to define the content interfaces for images. In MPEG-7, Dominant Color Descriptor (DCD) is considered as the most important feature, and is widely used to describe the color features of an image. To support semantic queries from users, we proposed a color feature semantic mapping method in this work, which can translate the DCD values into semantic color names. The semantic mapping method is realized by constructing a mapping table between the DCD values and the semantic color names. To validate the effectiveness of our mapping method, an image retrieval experiment is conducted. From the comparison with the manually indexed description, the proposed mapping method is proved to be effective by the experiment results. Our work is very important to automatically generate the semantic description of an image and then support the users’ semantic retrieval queries.

scholarly journals Image Retrieval Based on Content Using Color Feature

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Ahmed J. Afifi ◽  
Wesam M. Ashour
Keyword(s):  
Image Retrieval ◽  
Image Enhancement ◽  
Clustering Algorithm ◽  
Content Based Image Retrieval ◽  
Visual Feature ◽  
Color Feature ◽  
Retrieval Time ◽  
Speed Up

Content-based image retrieval from large resources has become an area of wide interest in many applications. In this paper we present a CBIR system that uses Ranklet Transform and the color feature as a visual feature to represent the images. Ranklet Transform is proposed as a preprocessing step to make the image invariant to rotation and any image enhancement operations. To speed up the retrieval time, images are clustered according to their features using k-means clustering algorithm.

scholarly journals Comparing the impact of Clustering with Content Based Image Retrieval Approaches for Plant Identification

2013 ◽  
Vol 9 (1) ◽  
pp. 985-994
Author(s):  
Komal Asrani ◽  
Renu Jain
Keyword(s):  
Image Retrieval ◽  
Processing Time ◽  
Search Time ◽  
Human Perception ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Plant Identification ◽  
Retrieval Time ◽  
The Impact ◽  
Leaf Image

Contour Based retrieval of images is an active and challenging field of research.  Among various parameters available for contour based image retrieval, shape is considered an important aspect because it is closest to the human perception. Most of the shape based image retrieval methods require large processing time for generating accurate results due to huge database. To reduce the search time, we have divided the database into clusters on the basis of eccentricity of leaf using K-Means approach. After making the clusters, different contour based approaches are applied for leaf/plant identification and results are compared.  The leaf image is processed to generate feature vectors which are stored in database.  We have used Swedish leaf image database (SLID) consisting of 15 species with 75 leaves per class and total of 1125 leaf images. In this paper, we compare results of contour based retrieval approaches with and without clustering. From these results, it is found that by incorporating clustering, performance of contour based retrieval approaches remains same but retrieval time is reduced.

scholarly journals Content-Based Image Retrieval System for Real Images

10.29007/w4sr ◽  
2018 ◽  
Author(s):  
Yin-Fu Huang ◽  
Bo-Rong Chen
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Database ◽  
Multimedia Data ◽  
Content Based Image Retrieval ◽  
Real Image ◽  
Query Image ◽  
Image Retrieval System ◽  
Similar Images ◽  
Network Technologies

With the rapid progress of network technologies and multimedia data, information retrieval techniques gradually become content-based, and not text-based yet. In this paper, we propose a content-based image retrieval system to query similar images in a real image database. First, we employ segmentation and main object detection to separate the main object from an image. Then, we extract MPEG-7 features from the object and select relevant features using the SAHS algorithm. Next, two approaches “one-against- all” and “one-against-one” are proposed to build the classifiers based on SVM. To further reduce indexing complexity, K-means clustering is used to generate MPEG-7 signatures. Thus, we combine the classes predicted by the classifiers and the results based on the MPEG-7 signatures, and find out the similar images to a query image. Finally, the experimental results show that our method is feasible in image searching from the real image database and more effective than the other methods.

scholarly journals Unsupervised Content Based Image Retrieval by Combining Visual Features of an Image With A Threshold

2012 ◽  
pp. 149-154
Author(s):  
S. M. Zakariya ◽  
Rashid Ali ◽  
Nesar Ahmad
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Database ◽  
The Other ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Query Image ◽  
System A ◽  
Image Retrieval System

Content-based image retrieval (CBIR) uses the visual features of an image such as color, shape and texture to represent and index the image. In a typical content based image retrieval system, a set of images that exhibit visual features similar to that of the query image are returned in response to a query. CLUE (CLUster based image rEtrieval) is a popular CBIR technique that retrieves images by clustering. In this paper, we propose a CBIR system that also retrieves images by clustering just like CLUE. But, the proposed system combines all the features (shape, color, and texture) with a threshold for the purpose. The combination of all the features provides a robust feature set for image retrieval. We evaluated the performance of the proposed system using images of varying size and resolution from image database and compared its performance with that of the other two existing CBIR systems namely UFM and CLUE. We have used four different resolutions of image. Experimentally, we find that the proposed system outperforms the other two existing systems in ecery resolution of image.

scholarly journals A Review on LBP in Image Retrieval System for Future Enhancement and Vector Images

2020 ◽  
pp. 155-161
Author(s):  
K Rajalakshmi ◽  
V Krishna Dharshini ◽  
S Selva Meena
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Content Based Image Retrieval ◽  
Large Set ◽  
Query Image ◽  
Matching Algorithm ◽  
Image Retrieval System ◽  
Similar Images ◽  
Vector Images ◽  
Similar Kind

Content-Based Image Retrieval is a process to retrieve the similar images from the large set of image database corresponding to the query image. In CBIR low level or pixel level features such as color, texture and shape of the images are extracted and on the basis of similarity matching algorithm the required similar kind of images are retrieved from the image database. To understand the evaluation and evolution of CBIR system various research was studied and various research is going on this way also. In this paper, we have discussed some of the popular pixel level feature extraction techniques for Content-Based Image Retrieval and we also present here about the performance of each technique.

scholarly journals Effectiveness of MPEG-7 Color Features in Clothing Retrieval

2017 ◽  
Vol 6 (2) ◽  
pp. 166-173
Author(s):  
Arsy Febrina Dewi ◽  
Fitri Arnia ◽  
Rusdha Muharar
Keyword(s):  
Image Retrieval ◽  
Human Perception ◽  
Extraction Process ◽  
Image Features ◽  
The Body ◽  
Human Vision ◽  
Dominant Color ◽  
Women's Clothing ◽  
Color Features ◽  
Color Descriptor

Clothing is a human used to cover the body. Clothing consist of dress, pants, skirts, and others. Clothing usually consists of various colors or a combination of several colors. Colors become one of the important reference used by humans in determining or looking for clothing according to their wishes. Color is one of the features that fit the human vision. Content Based Image Retrieval (CBIR) is a technique in Image Retrieval that give index to an image based on the characteristics contained in image such as color, shape, and texture. CBIR can make it easier to find something because it helps the grouping process on image based on its characteristic. In this case CBIR is used for the searching process of Muslim fashion based on the color features. The color used in this research is the color descriptor MPEG-7 which is Scalable Color Descriptor (SCD) and Dominant Color Descriptor (DCD). The SCD color feature displays the overall color proportion of the image, while the DCD displays the most dominant color in the image. For each image of Muslim women's clothing, the extraction process utilize SCD and DCD. This study used 150 images of Muslim women's clothing as a dataset consistingclass of red, blue, yellow, green and brown. Each class consists of 30 images. The similarity between the image features is measured using the eucludian distance. This study used human perception in viewing the color of clothing.The effectiveness is calculated for the color features of SCD and DCD adjusted to the human subjective similarity. Based on the simulation of effectiveness DCD result system gives higher value than SCD.

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