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

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

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

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.

Color Filter Based on Image ROI Extraction

With the rapid development of Internet, more and more enterprises establish business sites to achieve the purpose of online transactions. Taking taobao.com for example, hundreds of millions of goods trade on the trading platform. In front of the huge commodity image database, extraction of image features is very convenient for people to find out images of user requirement. This paper focus mainly on the color feature of images. Firstly, we segment ROI of images using grabCut algorithm; secondly, we extract primary color of images by using dominant color descriptor of MPEG 7; Thirdly, we adopt RGB color quantization to quantize the primary color. Finally achieve the purpose of image color navigation. I have done experiment to compare with some other methods, and find that the algorithms I adopted make a better performance.

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

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.