CONTENT-BASED IMAGE RETRIEVAL WITH RELEVANCE FEEDBACK USING ADAPTIVE PROCESSING OF TREE-STRUCTURE IMAGE REPRESENTATION

2003 ◽  
Vol 03 (01) ◽  
pp. 119-143 ◽  
Author(s):  
ZHIYONG WANG ◽  
ZHERU CHI ◽  
DAGAN FENG ◽  
AH CHUNG TSOI
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Network Model ◽  
Neural Network Model ◽  
Relevance Feedback ◽  
Image Representation ◽  
Tree Structure ◽  
Content Based Image Retrieval ◽  
Adaptive Processing ◽  
Image Content

Content-based image retrieval has become an essential technique in multimedia data management. However, due to the difficulties and complications involved in the various image processing tasks, a robust semantic representation of image content is still very difficult (if not impossible) to achieve. In this paper, we propose a novel content-based image retrieval approach with relevance feedback using adaptive processing of tree-structure image representation. In our approach, each image is first represented with a quad-tree, which is segmentation free. Then a neural network model with the Back-Propagation Through Structure (BPTS) learning algorithm is employed to learn the tree-structure representation of the image content. This approach that integrates image representation and similarity measure in a single framework is applied to the relevance feedback of the content-based image retrieval. In our approach, an initial ranking of the database images is first carried out based on the similarity between the query image and each of the database images according to global features. The user is then asked to categorize the top retrieved images into similar and dissimilar groups. Finally, the BPTS neural network model is used to learn the user's intention for a better retrieval result. This process continues until satisfactory retrieval results are achieved. In the refining process, a fine similarity grading scheme can also be adopted to improve the retrieval performance. Simulations on texture images and scenery pictures have demonstrated promising results which compare favorably with the other relevance feedback methods tested.

scholarly journals Convolutional neural networks for relevance feedback in content based image retrieval

2020 ◽  
Vol 79 (37-38) ◽  
pp. 26995-27021
Author(s):  
Lorenzo Putzu ◽  
Luca Piras ◽  
Giorgio Giacinto
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
Image Representation ◽  
Semantic Gap ◽  
Content Based Image Retrieval ◽  
Data Sets ◽  
Great Success ◽  
Query Image ◽  
Pros And Cons ◽  
The One

Abstract Given the great success of Convolutional Neural Network (CNN) for image representation and classification tasks, we argue that Content-Based Image Retrieval (CBIR) systems could also leverage on CNN capabilities, mainly when Relevance Feedback (RF) mechanisms are employed. On the one hand, to improve the performances of CBIRs, that are strictly related to the effectiveness of the descriptors used to represent an image, as they aim at providing the user with images similar to an initial query image. On the other hand, to reduce the semantic gap between the similarity perceived by the user and the similarity computed by the machine, by exploiting an RF mechanism where the user labels the returned images as being relevant or not concerning her interests. Consequently, in this work, we propose a CBIR system based on transfer learning from a CNN trained on a vast image database, thus exploiting the generic image representation that it has already learned. Then, the pre-trained CNN is also fine-tuned exploiting the RF supplied by the user to reduce the semantic gap. In particular, after the user’s feedback, we propose to tune and then re-train the CNN according to the labelled set of relevant and non-relevant images. Then, we suggest different strategies to exploit the updated CNN for returning a novel set of images that are expected to be relevant to the user’s needs. Experimental results on different data sets show the effectiveness of the proposed mechanisms in improving the representation power of the CNN with respect to the user concept of image similarity. Moreover, the pros and cons of the different approaches can be clearly pointed out, thus providing clear guidelines for the implementation in production environments.

scholarly journals Relevance Feedback Using RBF for Content Based Image Retrieval

2012 ◽  
Vol 6-7 ◽  
pp. 1150-1155
Author(s):  
Gui Zhi Li ◽  
Chang Sheng Zhou ◽  
Wei Wang ◽  
Ya Hui Liu
Keyword(s):  
Neural Network ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Rbf Neural Network ◽  
Feature Space ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Neural Network Learning ◽  
Multiple Queries ◽  
Semantic Relevance

Conventional approaches to image retrieval are based on the assumption that relevant images are physically near the query image in some feature space. However, semantically related images are often scattered across several visual clusters. This leads to adapting multiple queries to represent a query in the feature space. Therefore, it is necessary to handle disjunctive queries in the feature space. In this paper, a new content-based image retrieval method with relevance feedback technique using RBF neural network learning is proposed. The method transfers the process of relevance feedback into a learning problem of RBF neural network. RBFNN can describe the distribution of positive feedback sample images in feature space with a set of neighboring clusters produced through constructing neural network, for accurately reflecting their semantic relevance. The performance of the method using RBFNN is evaluated on a database of 10,000 images. Experimental results demonstrate the effectiveness of the proposed method.

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