SISTEM TEMU KEMBALI CITRA KAIN BERBASIS TEKSTUR DAN WARNA

Content Based Image Retrieval is a method to find images by comparing between a query image with images in database based on information of image. CBIR used to find images in database based on similarity of colors, texture and shapes. This research will using colors and texture methosd to find similarity images in database. Method that in using for colors extraction is HSV Histograms then for texture extraction is static characteristic extraction method. This research using 30 of images from 5 different type of cloth as training and query images. Result for image retrival After performing subjective test using recall method based on texture similarity percentages 76,19%, based on color percentages 100% and based on texture and color similarity percentages 100%. Result of this study is content based image retrieval based texture and color using static characteristic extraction and HSV Histograms method can to retrieve relevan of images in database that match by query image

A NEW HCL COLOR SPACE WITH ASSOCIATED COLOR SIMILARITY MEASURE FOR COLOR-BASED IMAGE RETRIEVAL

Color analysis is frequently used in image/video retrieval. However, many existing color spaces and color distances fail to capture correctly color differences usually perceived by the human eye. The objective of this paper is first to highlight the limitations of existing color spaces and similarity measures in representing human perception of colors, and then to propose (i) a new perceptual color space model called HCL, and (ii) an associated color similarity measure denoted DHCL. Experimental results show that using DHCL on the new color space leads to a solution very close to human perception of colors and hence to a potentially more effective content-based image/video retrieval. Moreover, the application of the similarity measure DHCL to other spaces like HSV leads to a better retrieval effectiveness. A comparison of HCL against L*C*H and CIECAM02 spaces using color histograms and a similarity distance based on Dirichlet distribution illustrates the good performance of HCL for a collection of 3500 images of different kinds.Key words : HCL color space, color analysi

Plant image retrieval using color, shape and texture features

We present a content-based image retrieval system for plant image retrieval, intended especially for the house plant identification problem. A plant image consists of a collection of overlapping leaves and possibly flowers, which makes the problem challenging.We studied the suitability of various well-known color, shape and texture features for this problem, as well as introducing some new texture matching techniques and shape features. Feature extraction is applied after segmenting the plant region from the background using the max-flow min-cut technique. Results on a database of 380 plant images belonging to 78 different types of plants show promise of the proposed new techniques and the overall system: in 55% of the queries, the correct plant image is retrieved among the top-15 results. Furthermore, the accuracy goes up to 73% when a 132-image subset of well-segmented plant images are considered

Video matching using DC-image and local features

This paper presents a suggested framework for video matching based on local features extracted from the DCimage of MPEG compressed videos, without decompression. The relevant arguments and supporting evidences are discussed for developing video similarity techniques that works directly on compressed videos, without decompression, and especially utilising small size images. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and the corresponding computation complexity. The second experiment compares between using local features and global features in video matching, especially in the compressed domain and with the small size images. The results confirmed that the use of DC-image, despite its highly reduced size, is promising as it produces at least similar (if not better) matching precision, compared to the full I-frame. Also, using SIFT, as a local feature, outperforms precision of most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the realtime margin. There are also various optimisations that can be done to improve this computation complexity

Autoencoding the Retrieval Relevance of Medical Images

Content-based image retrieval (CBIR) of medical images is a crucial task that can contribute to a more reliable diagnosis if applied to big data. Recent advances in feature extraction and classification have enormously improved CBIR results for digital images. However, considering the increasing accessibility of big data in medical imaging, we are still in need of reducing both memory requirements and computational expenses of image retrieval systems. This work proposes to exclude the features of image blocks that exhibit a low encoding error when learned by a $n/p/n$ autoencoder ($p\!<\!n$). We examine the histogram of autoendcoding errors of image blocks for each image class to facilitate the decision which image regions, or roughly what percentage of an image perhaps, shall be declared relevant for the retrieval task. This leads to reduction of feature dimensionality and speeds up the retrieval process. To validate the proposed scheme, we employ local binary patterns (LBP) and support vector machines (SVM) which are both well-established approaches in CBIR research community. As well, we use IRMA dataset with 14,410 x-ray images as test data. The results show that the dimensionality of annotated feature vectors can be reduced by up to 50% resulting in speedups greater than 27% at expense of less than 1% decrease in the accuracy of retrieval when validating the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015, Orleans, Franc

DC-image for real time compressed video matching

This chapter presents a suggested framework for video matching based on local features extracted from the DC-image of MPEG compressed videos, without full decompression. In addition, the relevant arguments and supporting evidences are discussed. Several local feature detectors will be examined to select the best for matching using the DC-image. Two experiments are carried to support the above. The first is comparing between the DC-image and I-frame, in terms of matching performance and computation complexity. The second experiment compares between using local features and global features regarding compressed video matching with respect to the DC-image. The results confirmed that the use of DC-image, despite its highly reduced size, it is promising as it produces higher matching precision, compared to the full I-frame. Also, SIFT, as a local feature, outperforms most of the standard global features. On the other hand, its computation complexity is relatively higher, but it is still within the real-time margin which leaves a space for further optimizations that can be done to improve this computation complexity