and Engg

There are many methods to retrieve an image from an amassment of images in the database in order to meet users demand with image content kindred attribute, edge pattern homogeneous attribute, color homogeneous attribute, etc. An image retrieval system offers an efficient way to access or retrieve set of similar images by directly computing the image features from images by directly computing the image features from an image as reported by utilizing different kinds of techniques as well as algorithm. Content based image retrieval (CBIR) is most recently used technique for image retrieval from large image database. The reason behind content based image retrieval is to get perfect and fast result. There are many technique of CBIR utilized for image retrieval. A Block Truncation Coding technique is the famous method used for image retrieval. In the proposed system the advanced technique of BTC is used that is Ordered Dither Block Truncation Coding (ODBTC). ODBTC encoded data stream to construct the image features namely Color Co-occurrence and Bit Pattern features. After the extraction of this feature similarity distance is computed for retrieving a set of similar images. And to make the search more accurate K-means clustering method is used. The most similar images to the query image are selected and these features are being appended together and k-means clustering is applied. This method retrieves more similar images to the query image than the first search. The proposed scheme can be considered as very good in color image retrieval application. The process is implemented in a MATLAB 2014.

International Journal of Engineering and Advanced Technology, 2020

With tremendous growth in social media and digital technologies, generation, storing and transfer of huge amount of information over the internet is on the rise. Images or visual mode of communication have been prevailing and widely accepted as a mode of communication since ages. And with the growth of internet, the rate at which images are generated is growing exponentially. But the methods used to retrieve images are still very slow and inefficient, compared to the rate of increase in image databases. To cope up with this explosive increase in images, this information age has seen huge research advancement in Content Based Image Retrieval (CBIR). CBIR systems provide a way of utilizing the 3 major ways in which content is portrayed in images, those are shape, texture and color. In CBIR system, features are extracted from query image and similarity is found with features stored in database for retrieval. This provides an objective way of image retrieval, which is more efficient com...

2013

Proficient Content Based Image Retrieval system is a much needed system for plenty of image retrieval supporting applications, which retrieve images more rapidly from giant image databases. Block Truncation Coding (BTC) is one of the prominent and simple techniques used most of the time. This method is built on color properties of an image for image retrieval. In this paper a novel Static Thepade's Ternary Block Truncation Coding with degree values (varied as n=1 to 5) and Dynamic Thepade's Ternary Block Truncation Coding are proposed. Total 36 variations of proposed image retrieval methods, using five degree values for Static Thepade's Block Truncation Coding and Dynamic Thepade's Block Truncation Coding with six color spaces (RGB, LUV, YCgCb, YCbCr, YUV and YIQ) are experimented on image database having 1000 images in 10 different categories. Experimental results have shown better performance in YCbCr color space followed by YUV. The best image retrieval is by Dyna...

—The current study deals with deriving of image feature descriptor by error diffusion based block truncation coding (EDBTC). The image feature descriptor is basically comprised by the two error diffusion block truncation coding, color quantizers and its equivalent bitmap image. The bitmap image distinguish the image edges and textural information of two color quantizers to signify the color allocation and image contrast derived by the Bit Pattern Feature and Color Co-occurrence Feature. Tentative outcome reveal the benefit of proposed feature descriptor as contrast to existing schemes in image retrieval assignment under normal and textural images. The Error-Diffusion Block Truncation Coding method compresses an image efficiently, and at the same time, its consequent compacted information flow can provides an efficient feature descriptor intended for operating image recovery and categorization. As a result, the proposed design preserves an effective candidate for real-time image retrieval applications.

—The elementary technology for video retrieval technique in computing is very sensitive to the user's query not to the contents of video. Every user has perspective to the video in the world of videos. So to identify this is difficult using text based video retrieval as this retrieval is based on text as processing input for video retrieval. The better approach for this video retrieval is content based video retrieval which is gaining acceptance in world of videos as a technique. In this paper, a novel approach for increasing the performance of retrieval by using intermediate levels with Block Truncation Coding (BTC) is presented. For this, the experimental data set is built up of 10 different categories in which 50 videos are present per category. In this paper, the approach of generation of feature by using 20 th frequency frames of videos from data set considered. Each of the extracted frame is intermediated by intermediate blocks (which is considered as 4 and 9) and then BTC applied on each block to get feature vector suing on 7 different color spaces as KLUV, YCbCr, YCgCb, YIQ, YUV RGB, and XYZ. The feature vectors generated are processed and best match results are achieved. The similarity criteria used is absolute difference (AD) for calculating similarity or difference among videos and query. The performance of this technique is confirmed with help of cross over points of precision and recall values for each color space. The higher performance is achieved is in YIQ color space using intermediate block level 16.

As per the recent survey, it is noticed that image storage and retrieval on the World Wide Web (WWW) has made significant progress. The present searching systems have still confronts for us while searching the images from the web. In this paper, a new idea to extract the images from the image database is presented to achieve better performance. The proposed CBIR system is implemented by considering the low level visual features of an image. The low level features are obtained by converting the color image into a grayscale image. The color features are obtained using modified Block Truncation Coding (BTC) method. The four statistical properties (energy, contrast, entropy and uniformity) of the texture extracted as a second low level features. The third visual property of image is extracted as shape feature using shape descriptor. Finally, an experiment is conducted on the ground truth image database that has more than 1000 images of different categories. In our previous work a middle range of block size has given best performance as color features. A block size of 32 x 32 is chosen for the experiment to enhance the performance of the system. The proposed system performance has been improved by combining these three features.

2016

This technology presents a technique for content-based image retrieval (CBIR) by misusing the benefit of low complexity ordered dither block truncation coding (ODBTC) for the generation of image content descriptor. In the encoding step, ODBTC packs a picture hinder into relating quantizers and bitmap picture. Two picture elements are proposed to list a picture, in particular, color co-occurrence feature (CCF) and bit pattern features (BPF), which are created specifically from the ODBTC encoded information streams without performing the disentangling procedure. The CCF and BPF of a picture are essentially gotten from the two ODBTC quantizers and bitmap, individually, by including the visual codebook. Experimental results show that the proposed method is superior to the block truncation coding image retrieval systems and the other earlier methods, and thus prove that the ODBTC arrangement is not simply suited for picture weight, because of its ease, also offers a direct and fruitful d...

International Journal of Computer Applications, 2013

The volume of video data is increasing because of advanced technology in imaging communication. This tends to increase the popularity the videos. The conventional textual description which is based on text entered by user, so, suffers from user perception. Hence, content based video retrieval is more attractive than text based retrieval, as it gives result proximity to semantic view. Here, a novel colour content based video retrieval based on block truncation coding is proposed with the help of seven assorted colour spaces. Video database of 500 video samples spread across 10 categories is considered as a test bed for experimentation. Performance comparison is done with the help of cross-over point of average precision and average recall values of 500 query videos fired on the whole database. Experimental results have shown performance improvement for video retrieval in chromaticityluminance colour spaces as compared conventional colour space (RGB). The best performance is observed by Block Truncation Coding withKekre's LUV colour space followed by YCbCr colour space.

International Journal For Science Technology And Engineering, 2016

Many image retrieval systems use global features to retrieve images such as color and texture. But the previous results provide too many false positives while using these global features to search for similar images. Content-based image retrieval system works with the entire image and searching is based on comparison of the query image with image database. CBIR uses the visual information of an image such as color, texture and edge to represent and index the image. These contents can be obtained by applying color, texture and edge based techniques. These techniques are applied on both query image and database to get an image from the database more accurately. Color images are indexed by error diffusion block truncation coding (EDBTC). To improve the accuracy of color histogram based matching YCbCr color space is added into EDBTC indexing scheme. Texture analysis is more important because it is used to improve the discriminatory ability of the extracted image features. The speed of e...

Content based image retrieval (CBIR) is a technique of retrieving similar images to that of query image from a collection of dataset. CBIR system uses visual features of image to retrieve the similar images from dataset. Over the past years, many researches were applied for developing efficient image feature descriptors. This study presents a technique for improving image retrieval by utilizing a data compression method called ordered dither block truncation coding (ODBTC). Image content descriptors are generated from ODBTC compressed data stream. The ODBTC produces two color quantizers and a single bitmap image. Two image features, namely Color co-occurrence Feature (CCF) and Bit Pattern Feature (BPF) are generated from the output of ODBTC. The similarities between two images are measured from their CCF and BPF features. The proposed method is implemented on Raspberry Pi3 model B in OpenCV-Python platform. Experimental results show that the data compression method described in this paper can be used for image retrieval applications also.