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.

Main color extraction algorithm and its application in clothing image retrieval

2021 ◽  
pp. 1-8
Author(s):  
Hong Zhang
Keyword(s):  
Image Retrieval ◽  
Association Rules ◽  
Recall Rate ◽  
Image Features ◽  
Analysis Model ◽  
Dominant Color ◽  
Color Ratio ◽  
Color Features ◽  
Extraction Algorithm ◽  
Main Color

In content-based clothing image retrieval, color features can best reflect the basic characteristics of clothing, and also the most stable visual features. Compared with other image features, color features have smaller size, orientation and visual dependence. This paper studies the application of dominant color extraction algorithm in clothing image retrieval, and proposes a clothing classification method based on dominant color ratio. Clothing image is divided into color clothing and non color clothing. On this basis, a main color extraction algorithm of clothing image color feature extraction is proposed. Taking the clothing color features as an example, the image features are analyzed, and then the SVM image classification algorithm is designed to analyze the image features. Then an improved scheme based on data mining technology is proposed, and the analysis model based on association rules is established. Finally, a method of standard man hour correction based on association rules is proposed. The experimental results show that, compared with the existing algorithms, the recall rate and accuracy rate are significantly improved for the clothing with simple or complex background, pattern and non pattern clothing. Analyze and divide the specific areas of clothing image, extract the main color of clothing image, share and recommend clothing image and color extraction results. This research not only has certain research significance, but also has certain practical application value.

scholarly journals Penerapan Deskriptor Warna Dominan untuk Temu Kembali Citra Busana pada Peranti Bergerak

2016 ◽  
Vol 12 (3) ◽  
pp. 104 ◽  
Author(s):  
Yustina Dhyanti ◽  
Khairul Munadi ◽  
Fitri Arnia
Keyword(s):  
Image Retrieval ◽  
Euclidean Distance ◽  
Superior Performance ◽  
Dominant Color ◽  
Color Features ◽  
Color Descriptor ◽  
Color Correlogram ◽  
Dominant Color Descriptor ◽  
Color Combinations ◽  
Potential Customers

Nowadays, clothes with various designs and color combinations are available for purchasing through an online shop, which is mostly equipped with keyword-based item retrieval. Here, the object in the online database is retrieved based on the keyword inputted by the potential buyers. The keyword-based search may bring potential customers on difficulties to describe the clothes they want to buy. This paper presents a new searching approach, using an image instead of text, as the query into an online shop. This method is known as content-based image retrieval (CBIR).  Particularly, we focused on using color as the feature in our Muslimah clothes image retrieval. The dominant color descriptor (DCD) extracts the wardrobe's color. Then, image matching is accomplished by calculating the Euclidean distance between the query and image in the database, and the last step is to evaluate the performance of the DWD by calculating precision and recall. To determine the performance of the DCD in extracting color features, the DCD is compared with another color descriptor, that is dominant color correlogram descriptor (DCCD). The values of precision and recall of DCD ranged from 0.7 to 0.9 while the precision and recall of DCCD ranged from 0.7 to 0.8. These results showed that the DCD produce a superior performance compared to DCCD in retrieving a set of clothing image, either plain or patterned colored clothes.

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

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.

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 An Effective Contour Detection based Image Retrieval using Multi-Fusion Method and Neural Network

2021 ◽  
Author(s):  
Rohit Raja ◽  
Sandeep Kumar ◽  
Shilpa Choudhary ◽  
Hemlata Dalmia
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Texture Features ◽  
Visual Image ◽  
Image Features ◽  
Contour Detection ◽  
Image Feature ◽  
Digital Platforms ◽  
Color Descriptor ◽  
Dominant Color Descriptor

Abstract Day by day, rapidly increasing the number of images on digital platforms and digital image databases has increased. Generally, the user requires image retrieval and it is a challenging task to search effectively from the enormous database. Mainly content-based image retrieval (CBIR) algorithm considered the visual image feature such as color, texture, shape, etc. The non-visual features also play a significant role in image retrieval, mainly in the security concern and selection of image features is an essential issue in CBIR. Performance is one of the challenging tasks in image retrieval, according to current CBIR studies. To overcome this gap, the new method used for CBIR using histogram of gradient (HOG), dominant color descriptor (DCD) & hue moment (HM) features. This work uses color features and shapes texture in-depth for CBIR. HOG is used to extract texture features. DCD on RGB and HSV are used to improve efficiency and computation. A neural network (NN) is used to extract the image features, which improves the computation using the Corel dataset. The experimental results evaluated on various standard benchmarks Corel-1k, Corel-5k datasets, and outcomes of the proposed work illustrate that the proposed CBIR is efficient for other state-of-the-art image retrieval methods. Intensive analysis of the proposed work proved that the proposed work has better precision, recall, accuracy

scholarly journals Association-Based Image Retrieval

2011 ◽  
pp. 379-406
Author(s):  
Arun Kulkarni ◽  
Leonard Brown
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Human Perception ◽  
Human Memory ◽  
Image Features ◽  
Multimedia Data ◽  
Query Image ◽  
Storage And Retrieval ◽  
Image Storage And Retrieval ◽  
High Level

With advances in computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed. In order to extract useful information from this huge amount of data, many content-based image retrieval (CBIR) systems have been developed in the last decade. A Typical CBIR system captures image features that represent image properties such as color, texture, or shape of objects in the query image and try to retrieve images from the database with similar features. Recent advances in CBIR systems include relevance feedback based interactive systems. The main advantage of CBIR systems with relevance feedback is that these systems take into account the gap between the high-level concepts and low-level features and subjectivity of human perception of visual content. CBIR systems with relevance feedback are more efficient than conventional CBIR systems; however, these systems depend on human interaction. In this chapter, we describe a new approach for image storage and retrieval called association-based image retrieval (ABIR). The authors try to mimic human memory. The human brain stores and retrieves images by association. They use a generalized bi-directional associative memory (GBAM) to store associations between feature vectors that represent images stored in the database. Section I introduces the reader to the CBIR system. In Section II, they present architecture for the ABIR system, Section III deals with preprocessing and feature extraction techniques, and Section IV presents various models of GBAM. In Section V, they present case studies.

scholarly journals A Journey from basic Image Features to Lofty Human Intelligence in Content-based Image Retrieval: Motivation, Applications and Future Trends

2020 ◽  
Vol 9 (2) ◽  
pp. 990-998
Keyword(s):  
Image Retrieval ◽  
Spatial Information ◽  
Similarity Measures ◽  
Human Perception ◽  
Image Features ◽  
Content Based Image Retrieval ◽  
Future Trends ◽  
Research Issues ◽  
Basic Image ◽  
High Level

Due to a remarkable increase in the complexity of the multimedia content, there is a cumulative enhancement of digital images both online and offline. For the purpose of retrieving images from a vast storehouse of images, there is an urgent requirement of an effectual image retrieval system and the most effective system in this domain is denoted as content-based image retrieval (CBIR) system. CBIR system is generally based on the extraction of basic image attributes like texture, color, shape, spatial information, etc. from an image. But, there exists a semantic gap between the basic image features and high-level human perception and to reduce this gap various techniques can be used. This paper presents a detailed study about the various basic techniques with an emphasis on different intelligent techniques like, the usage of machine learning, deep learning, relevance feedback, etc., which can be used to achieve a high level semantic information in CBIR systems. In addition, a detailed outline regarding the framework of a basic CBIR system, various benchmark datasets, similarity measures, evaluation metrics have been also discussed. Finally, solution to some research issues and future trends have also been given in this paper.

Color Extraction and Research of Image Retrieval Based on Wavelet Territory

2014 ◽  
Vol 631-632 ◽  
pp. 418-421
Author(s):  
Lin Lin Song ◽  
Qing Hu Wang ◽  
Zhi Li Pei
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Low Frequency ◽  
Histogram Method ◽  
Color Feature ◽  
Dominant Color ◽  
Color Features ◽  
Human Eyes

This paper firstly studies the image color features based on wavelet territory. We introduce a color features’ extract method based on HSI low-frequency subband color features after partition. Firstly, according to the image attention from human eyes, we split the image into sub-blocks. Then extract HSI low-frequency subband color features of each sub-block after wavelet transform, and we can obtain the image color features by weighting. Comparing with traditional histogram method, the experiment results show that the proposed algorithm based on weighted dominant color feature has better retrieval precision.

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