Inter-query semantic learning approach to image retrieval

Fechser, Scott; Chang, Ran; Qi, Xiaojun

doi:10.1109/icassp.2010.5495405

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…By combining the high-level and low-level features, the performance of retrieval can be significantly enhanced. In the study in [20], the feedback information of each query is saved in the "concept database" as additional semantic information of images in the database. With accumulating information learned from previous feedback in queries, the performance of retrieval can be further improved.…”

Section: Feedback-based Image Retrievalmentioning

confidence: 99%

Multiple User Behavior Learning for Enhancing Interactive Image Retrieval

Cheung¹,

Chan²

2022

Preprint

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The existing image retrieval approaches focus on the behavior of a single user only in each query without considering the correlation of the behaviors of multiple users in performing similar queries. In fact, users would have similar behaviors while they have similar expectations during queries. Accordingly, this paper therefore proposes the interactive image retrieval framework with the Similar Behavior Learning model. The framework consists of two stages. In the first stage, the framework retrieves images with the content-based feature vector as preliminary query result for user selection. In the second stage, the SBL model determines the similarity of the user behavior and annotates label code to the selected images instantly. The images are indexed by label code can be retrieved more efficiently. Meanwhile, the selected images in preliminary result are used as additional information for retrieving better results at the end of the current query. Experiments show the promising results.

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Section: Feedback-based Image Retrievalmentioning

confidence: 99%

Multiple User Behavior Learning for Enhancing Interactive Image Retrieval

Cheung¹,

Chan²

2022

Preprint

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“…Finally, they infer relationships between images by analyzing the transformed matrix and estimate the semantic relevance level of a database image to the current query. Representative long-term learning techniques include retrieval pattern-based and feature vector model-based learning (Fechser et al, 2010). For example, Hoi et al (2006) apply the statistical correlation on the feedback log to analyze the relationship among the current and past retrieval sessions.…”

Section: Introductionmentioning

confidence: 99%

A Scalable Graph-Based Semi-Supervised Ranking System for Content-Based Image Retrieval

Chang

2013

International Journal of Multimedia Data Engineering and Management

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The authors propose a scalable graph-based semi-supervised ranking system for image retrieval. This system exploits the synergism between relevance feedback based transductive short-term learning and semantic feature-based long-term learning to improve retrieval performance. Active learning is applied to build a dynamic feedback log to extract semantic features of images. Two-layer manifold graphs are then built in both low-level visual and high-level semantic spaces. One graph is constructed at the first layer using anchor images obtained from the feedback log. Several graphs are constructed at the second layer using images in their respective cluster formed around each anchor image. An asymmetric relevance vector is created for each second layer graph by propagating initial scores from the first layer. These vectors are fused to propagate relevance scores of labeled images to unlabeled images. The authors’ extensive experiments demonstrate the proposed system outperforms four manifold-based and five state-of-the-art long-term-based image retrieval systems.

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“…The information in the log is usually aggregated into a semantic, relevance, or affinity matrix, which allows the CBIR system to discover extra knowledge (i.e., the semantic relevance level of a database image to the current query). Representative long-term learning techniques [9] include retrieval pattern-based learning and feature vector model-based learning. Here, we briefly review several long-term techniques that are related to the proposed approach.…”

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confidence: 99%

Complementary relevance feedback-based content-based image retrieval

Xiao

2013

Multimed Tools Appl

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We propose a complementary relevance feedback-based content-based image retrieval (CBIR) system. This system exploits the synergism between short-term and longterm learning techniques to improve the retrieval performance. Specifically, we construct an adaptive semantic repository in long-term learning to store retrieval patterns of historical query sessions. We then extract high-level semantic features from the semantic repository and seamlessly integrate low-level visual features and high-level semantic features in shortterm learning to effectively represent the query in a single retrieval session. The high-level semantic features are dynamically updated based on users' query concept and therefore represent the image's semantic concept more accurately. Our extensive experimental results demonstrate that the proposed system outperforms its seven state-of-the-art peer systems in terms of retrieval precision and storage space on a large scale imagery database.

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Inter-query semantic learning approach to image retrieval

Cited by 4 publications

References 11 publications

Multiple User Behavior Learning for Enhancing Interactive Image Retrieval

Multiple User Behavior Learning for Enhancing Interactive Image Retrieval

A Scalable Graph-Based Semi-Supervised Ranking System for Content-Based Image Retrieval

Complementary relevance feedback-based content-based image retrieval

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