Active learning paradigms for CBIR systems based on optimum-path forest classification

Silva, Andre Tavares da; Falcão, Alexandre X.; Magalhães, Leo Pini

doi:10.1016/j.patcog.2011.04.026

Cited by 46 publications

(17 citation statements)

References 17 publications

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“…Their efficiency (related with response time) and effectiveness (related with users' satisfaction) are very important for evaluating the quality of this CBIR system. In our prototype, we use the optimum-path Forest (OPF) [11,21,22] classifier for query and classification. OPF works by modeling the classification as a graph partition in a given feature space.…”

Section: The Rf-opf Prototypementioning

confidence: 99%

“…With the prototypes as the roots and the non-prototypes as the intermediate and terminal nodes, the optimum trees are built, which constitute the optimum-path forest (OPF). Compared with SVM, ANN-MLP and K-NN, OPF is usually superior to ANN-MLP and K-NN in accuracy and significantly outperforms SVM in computation time [11,21,22], which is very important in a prototype based on RF approach that generates results in a dialogic fashion.…”

Section: The Rf-opf Prototypementioning

confidence: 99%

“…We further apply optimumpath forest algorithm to perform image search procedures. By incorporating the relevance feedback approach into the OPF process [11], our method enables users to search for images based on his or her subjective preferences on collar design. To validate the above proposed ideas and methods, a series of experiments are conducted.…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of clothing images based on relevance feedback with focus on collar designs

Toyoura

Shimizu

et al. 2016

Vis Comput

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The content-based image retrieval methods are developed to help people find what they desire based on preferred images instead of linguistic information. This paper focuses on capturing the image features representing details of the collar designs, which is important for people to choose clothing. The quality of the feature extraction methods is important for the queries. This paper presents several new methods for the collar-design feature extraction. A prototype of clothing image retrieval system based on relevance feedback approach and optimum-path forest algorithm is also developed to improve the query results and allows users to find clothing image of more preferred design. A series of experiments are conducted to test the qualities of the feature extraction methods and validate the effectiveness and efficiency of the RF-OPF prototype from multiple aspects. The evaluation scores of initial query results are used to test the qualities of the feature extraction methods. The average scores of all RF steps, the average numbers of RF iterations taken before achieving desired results and the score transition of RF iterations are used to validate the effectiveness and efficiency of the proposed RF-OPF prototype.

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Section: The Rf-opf Prototypementioning

confidence: 99%

Section: The Rf-opf Prototypementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Retrieval of clothing images based on relevance feedback with focus on collar designs

Toyoura

Shimizu

et al. 2016

Vis Comput

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“…Such techniques select a batch of points simultaneously from an unlabeled set for manual labeling and are effective in utilizing the presence of parallel labeling agents and avoiding frequent classifier updates. Sample applications of BMAL include content based image retrieval [95,96], medical image classification [97] and text classification [98].…”

Section: Batch Mode Active Learning (Bmal)mentioning

confidence: 99%

Batch Mode Active Learning for Multimedia Pattern Recognition

Chakraborty

Balasubramanian

Panchanathan

2012

2012 IEEE International Symposium on Multimedia

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The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human la-

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“…The objective of CBIR systems is to provide relevant collection images by taking into account their similarity to user-defined query patterns (e.g., sketch, example image). In these systems, similarity computation is based on features that are associated with visual properties such as shape, texture, and color [1,2]. The main challenge here consists in mapping low-level features to high-level concepts typically found within images, a problem named as semantic gap.…”

Section: Introductionmentioning

confidence: 99%

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

Pedronette

Calumby

Torres

2015

J Image Video Proc.

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The interaction of users with search services has been recognized as an important mechanism for expressing and handling user information needs. One traditional approach for supporting such interactive search relies on exploiting relevance feedbacks (RF) in the searching process. For large-scale multimedia collections, however, the user efforts required in RF search sessions is considerable. In this paper, we address this issue by proposing a novel semisupervised approach for implementing RF-based search services. In our approach, supervised learning is performed taking advantage of relevance labels provided by users. Later, an unsupervised learning step is performed with the objective of extracting useful information from the intrinsic dataset structure. Furthermore, our hybrid learning approach considers feedbacks of different users, in collaborative image retrieval (CIR) scenarios. In these scenarios, the relationships among the feedbacks provided by different users are exploited, further reducing the collective efforts. Conducted experiments involving shape, color, and texture datasets demonstrate the effectiveness of the proposed approach. Similar results are also observed in experiments considering multimodal image retrieval tasks.

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Active learning paradigms for CBIR systems based on optimum-path forest classification

Cited by 46 publications

References 17 publications

Retrieval of clothing images based on relevance feedback with focus on collar designs

Retrieval of clothing images based on relevance feedback with focus on collar designs

Batch Mode Active Learning for Multimedia Pattern Recognition

A semi-supervised learning algorithm for relevance feedback and collaborative image retrieval

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