A New Algorithm for Sketch-Based Fashion Image Retrieval Based on Cross-Domain Transformation

Due to the rise of e-commerce platforms, online shopping has become a trend. However, the current mainstream retrieval methods are still limited to using text or exemplar images as input. For huge commodity databases, it remains a long-standing unsolved problem for users to find the interested products quickly. Different from the traditional text-based and exemplar-based image retrieval techniques, sketch-based image retrieval (SBIR) provides a more intuitive and natural way for users to specify their search need. Due to the large cross-domain discrepancy between the free-hand sketch and fashion images, retrieving fashion images by sketches is a significantly challenging task. In this work, we propose a new algorithm for sketch-based fashion image retrieval based on cross-domain transformation. In our approach, the sketch and photo are first transformed into the same domain. Then, the sketch domain similarity and the photo domain similarity are calculated, respectively, and fused to improve the retrieval accuracy of fashion images. Moreover, the existing fashion image datasets mostly contain photos only and rarely contain the sketch-photo pairs. Thus, we contribute a fine-grained sketch-based fashion image retrieval dataset, which includes 36,074 sketch-photo pairs. Specifically, when retrieving on our Fashion Image dataset, the accuracy of our model ranks the correct match at the top-1 which is 96.6%, 92.1%, 91.0%, and 90.5% for clothes, pants, skirts, and shoes, respectively. Extensive experiments conducted on our dataset and two fine-grained instance-level datasets, i.e., QMUL-shoes and QMUL-chairs, show that our model has achieved a better performance than other existing methods.

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Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval

Procedings of the British Machine Vision Conference 2017 ◽

10.5244/c.31.46 ◽

2017 ◽

Cited By ~ 10

Author(s):

Kaiyue Pang ◽

Yi-zhe Song ◽

Tony Xiang ◽

Timothy Hospedales

Keyword(s):

Image Retrieval ◽

Generative Learning ◽

Fine Grained ◽

Cross Domain

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An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191122113801 ◽

2019 ◽

Vol 13 ◽

Author(s):

Shikha Bhardwaj ◽

Gitanjali Pandove ◽

Pawan Kumar Dahiya

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Retrieval ◽

Hybrid System ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Retrieval Accuracy ◽

Color Descriptor ◽

Benchmark Datasets ◽

Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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Deep transfer learning mechanism for fine-grained cross-domain sentiment classification

Connection Science ◽

10.1080/09540091.2021.1912711 ◽

2021 ◽

pp. 1-18

Author(s):

Zixuan Cao ◽

Yongmei Zhou ◽

Aimin Yang ◽

Sancheng Peng

Keyword(s):

Transfer Learning ◽

Sentiment Classification ◽

Learning Mechanism ◽

Fine Grained ◽

Cross Domain

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Color quantization and its impact on color histogram based image retrieval accuracy

2009 First International Conference on Networked Digital Technologies ◽

10.1109/ndt.2009.5272135 ◽

2009 ◽

Cited By ~ 14

Author(s):

Khouloud Meskaldji ◽

Samia Boucherkha ◽

Salim Chikhi

Keyword(s):

Image Retrieval ◽

Color Histogram ◽

Color Quantization ◽

Retrieval Accuracy

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An Image Clustering and Feedback-based Retrieval Framework

International Journal of Multimedia Data Engineering and Management ◽

10.4018/jmdem.2010111204 ◽

2010 ◽

Vol 1 (1) ◽

pp. 55-74 ◽

Cited By ~ 2

Author(s):

Chengcui Zhang ◽

Liping Zhou ◽

Wen Wan ◽

Jeffrey Birch ◽

Wei-Bang Chen

Keyword(s):

Image Retrieval ◽

Clustering Algorithm ◽

Image Database ◽

Image Clustering ◽

Single Object ◽

Image Region ◽

Retrieval Accuracy ◽

Retrieval Systems ◽

Object Based ◽

Region Matching

Most existing object-based image retrieval systems are based on single object matching, with its main limitation being that one individual image region (object) can hardly represent the user’s retrieval target, especially when more than one object of interest is involved in the retrieval. Integrated Region Matching (IRM) has been used to improve the retrieval accuracy by evaluating the overall similarity between images and incorporating the properties of all the regions in the images. However, IRM does not take the user’s preferred regions into account and has undesirable time complexity. In this article, we present a Feedback-based Image Clustering and Retrieval Framework (FIRM) using a novel image clustering algorithm and integrating it with Integrated Region Matching (IRM) and Relevance Feedback (RF). The performance of the system is evaluated on a large image database, demonstrating the effectiveness of our framework in catching users’ retrieval interests in object-based image retrieval.

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