scholarly journals A Method for Recognizing and Separating Trademark Image Outer Frames

2002 ◽  
Vol 6 (3) ◽  
pp. 116-125
Author(s):  
Koji Abe ◽  
◽  
Haruhiko Kimura ◽  
Hideo Nagashima ◽  
Taki Kanda ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Experimental Evaluation ◽  
Human Perception ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Registered Trademark ◽  
Outer Frame ◽  
Trademark Registration ◽  
Trademark Images

We present a method for recognizing the existence of outer frames in binary trademark images and segmenting a trademark that contains an outer frame into the frame and its inner figure, even if both touch. This focuses on the development of content-based image retrieval (CBIR) for trademark registration. Using our proposed method, CBIR systems examine the similarity between images using only main image components. This includes a study for describing image components. We detail criteria of trademark image outer frames and propose an algorithm for recognizing and segmenting outer frames based on the criteria. Experimental results using 1843 registered trademark images and experimental evaluation by 13 participants showed that 98.4% of recognitions agreed with human perception.

A Perceptual Approach for Image Representation and Retrieval

2011 ◽  
pp. 121-141
Author(s):  
Noureddine Abbadeni
Keyword(s):  
Image Retrieval ◽  
Image Representation ◽  
Rank Correlation ◽  
Human Perception ◽  
Image Data ◽  
Content Based Image Retrieval ◽  
Data Set ◽  
Psychometric Method ◽  
Results Merging ◽  
Spearman Rank Correlation Coefficient

This chapter describes an approach based on human perception to content-based image representation and retrieval. We consider textured images and propose to model the textural content of images by a set of features having a perceptual meaning and their application to content-based image retrieval. We present a new method to estimate a set of perceptual textural features, namely coarseness, directionality, contrast and busyness. The proposed computational measures are based on two representations: the original images representation and the autocovariance function (associated with images) representation. The correspondence of the proposed computational measures to human judgments is shown using a psychometric method based on the Spearman rank-correlation coefficient. The set of computational measures is applied to content-based image retrieval on a large image data set, the well-known Brodatz database. Experimental results show a strong correlation between the proposed computational textural measures and human perceptual judgments. The benchmarking of retrieval performance, done using the recall measure, shows interesting results. Furthermore, results merging/fusion returned by each of the two representations is shown to allow significant improvement in retrieval effectiveness.

Trademark Image Retrieval

2011 ◽  
pp. 82-114
Author(s):  
Wing-Yin Chau ◽  
Chia-Hung Wei ◽  
Yue Li
Keyword(s):  
Image Retrieval ◽  
Scale Space ◽  
Zernike Moments ◽  
Registration System ◽  
Novel Approach ◽  
Centroid Distance ◽  
Curvature Scale Space ◽  
The World ◽  
Trademark Registration ◽  
Trademark Images

With the rapid increase in the amount of registered trademarks around the world, trademark image retrieval has been developed to deal with a vast amount of trademark images in a trademark registration system. Many different approaches have been developed throughout these years in an attempt to develop an effective TIR system. Some conventional approaches used in content-based image retrieval, such as moment invariants, Zernike moments, Fourier descriptors and curvature scale space descriptors, have also been widely used in TIR. These approaches, however, contain some major deficiencies when addressing the TIR problem. Therefore, this chapter proposes a novel approach in order to overcome the major deficiencies of the conventional approaches. The proposed approach combines the Zernike moments descriptors with the centroid distance representation and the curvature representation. The experimental results show that the proposed approach outperforms the conventional approaches in several circumstances. Details regarding to the proposed approach as well as the conventional approaches are presented in this chapter.

scholarly journals Image Retrieval Based on Fractal Dictionary Parameters

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Yuanyuan Sun ◽  
Rudan Xu ◽  
Lina Chen ◽  
Xiaopeng Hu
Keyword(s):  
Computer Vision ◽  
Image Compression ◽  
Image Retrieval ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Efficient Management ◽  
Retrieval Rate ◽  
Statistical Indices ◽  
Visual Database

Content-based image retrieval is a branch of computer vision. It is important for efficient management of a visual database. In most cases, image retrieval is based on image compression. In this paper, we use a fractal dictionary to encode images. Based on this technique, we propose a set of statistical indices for efficient image retrieval. Experimental results on a database of 416 texture images indicate that the proposed method provides a competitive retrieval rate, compared to the existing methods.

FACE ANNOTATION FOR FAMILY PHOTO ALBUM MANAGEMENT

2003 ◽  
Vol 03 (01) ◽  
pp. 81-94 ◽  
Author(s):  
LONGBIN CHEN ◽  
BAOGANG HU ◽  
LEI ZHANG ◽  
MINGJING LI ◽  
HONGJIANG ZHANG
Keyword(s):  
Face Recognition ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Experimental Evaluation ◽  
Learning Algorithm ◽  
Content Based Image Retrieval ◽  
The Core ◽  
Photo Album ◽  
Face Similarity

In this paper, we propose a framework to semi-automatically annotate faces in family photo albums. The core of the framework is the features used to define face similarity and this results in the learning algorithm used to refine automatic face annotation. We have adopted similarity based search and relevance feedback ideas developed for content-based image retrieval and a set of simple yet effective color and texture based features, in addition to the traditional face recognition features, in performing candidate annotation search. The experimental evaluation of the proposed approach has been conducted with a family album of 1707 photos and the results show that the proposed approach is an effective and efficient one for semi-automatic family photo album annotation.

A NOVEL INDEXING AND ACCESS MECHANISM USING AFFINITY HYBRID TREE FOR CONTENT-BASED IMAGE RETRIEVAL IN MULTIMEDIA DATABASES

2007 ◽  
Vol 01 (02) ◽  
pp. 147-170 ◽  
Author(s):  
KASTURI CHATTERJEE ◽  
SHU-CHING CHEN
Keyword(s):  
Image Retrieval ◽  
Metric Spaces ◽  
Nearest Neighbor ◽  
Human Perception ◽  
Multimedia Databases ◽  
Multimedia Retrieval ◽  
Index Structure ◽  
Content Based Image Retrieval ◽  
K Nearest Neighbor ◽  
Computational Overhead

An efficient access and indexing framework, called Affinity Hybrid Tree (AH-Tree), is proposed which combines feature and metric spaces in a novel way. The proposed framework helps to organize large image databases and support popular multimedia retrieval mechanisms like Content-Based Image Retrieval (CBIR). It is efficient in terms of computational overhead and fairly accurate in producing query results close to human perception. AH-Tree, by being able to introduce the high level semantic image relationship as it is in its index structure, solves the problem of translating the content-similarity measurement into feature level equivalence which is both painstaking and error-prone. Algorithms for similarity (range and k-nearest neighbor) queries are implemented and extensive experiments are performed which produces encouraging results with low I/O and distance computations and high precision of query results.

scholarly journals Interactive Content Based Image Retrieval using Multiuser Feedback

2017 ◽  
Vol 1 (4) ◽  
pp. 165
Author(s):  
M. Premkumar ◽  
R. Sowmya
Keyword(s):  
Image Retrieval ◽  
Association Rules ◽  
Relevance Feedback ◽  
Association Rule ◽  
Association Rule Mining ◽  
Human Perception ◽  
Content Based Image Retrieval ◽  
Rule Mining ◽  
Query Image ◽  
Feedback Techniques

Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.

scholarly journals Content-based image retrieval in radiology: analysis of variability in human perception of similarity

2015 ◽  
Vol 2 (2) ◽  
pp. 025501 ◽  
Author(s):  
Jessica Faruque ◽  
Christopher F. Beaulieu ◽  
Jarrett Rosenberg ◽  
Daniel L. Rubin ◽  
Dorcas Yao ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Human Perception ◽  
Content Based Image Retrieval

A Retrieval Strategy for Texture Image

2014 ◽  
Vol 635-637 ◽  
pp. 1018-1025
Author(s):  
Chun Hua Qian ◽  
He Qun Qiang ◽  
Sheng Rong Gong
Keyword(s):  
Image Retrieval ◽  
Image Database ◽  
Image Space ◽  
Experimental Results ◽  
Content Based Image Retrieval ◽  
Texture Image ◽  
Retrieval Strategy ◽  
Wold Decomposition ◽  
Texture Information

Texture Information is widely used as one of the main low-layer features in the content-based image retrieval. In general, when the retrieval is carried out in texture image space, the same description method is adopted to regular and irregular texture images. As a large amount of regular and irregular texture images existed in the image database, it is very difficult to describe every texture with the same description method. In this paper, a retrieval strategy for texture image is proposed. The proposed strategy is divided into steps: First, classify texture images by Wold decomposition into regular and irregular texture images, then describe and retrieve them by regular and irregular texture description separately. Experimental results have showed that proposed strategy can improve classification and retrieval precision.

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.

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