Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning

Al-Mohamade, Abeer; Bchir, Ouiem; Ismail, Mohamed Maher Ben

doi:10.3390/jimaging6010002

“…Finally, it fuses eight attributes from the three groups of local homogeneity, color space and texture to improve the quality of image segmentation. Al mohamade et al proposed an image retrieval algorithm based on attribute weight learning [20]. It learns a weight for each visual attribute of the image, and uses these weights to narrow the gap between the extracted attributes and the user's high-level semantic information.…”

Section: Attribute Weight Learningmentioning

confidence: 99%

Piecewise Weighting Function for Collaborative Filtering Recommendation

Zhang¹,

Li²

2022

Preprint

0

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<p>It is not always reasonable to set a fixed weight value to an attribute (or a variable) because this cannot accurately maintain the similarity between users and leads to decrease the performance of collaborative filtering recommendation algorithms. In this paper, a piecewise weighting method is proposed with the hyper-class representation for improving the collaborative filtering recommendation. Specifically, a kernel function is first applied to map the original data into the kernel space, and to learn the weight of each attribute. And then, the hyper-class representation of the data is constructed to learn the weight of the segmented attribute value (hyper-class) for each attribute, so as to construct a piecewise weighting function. In addition, the piecewise weighting function is applied to calculate the similarity between users for a collaborative filtering recommendation. Finally, experiments are conducted to examine the collaborative filtering recommendation algorithm, and show that the proposed algorithm with piecewise weighting functions is more efficient than the compared algorithm with fixed weight values, in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Precision.</p>

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“…Finally, it fuses eight attributes from the three groups of local homogeneity, color space and texture to improve the quality of image segmentation. Al mohamade et al proposed an image retrieval algorithm based on attribute weight learning [20]. It learns a weight for each visual attribute of the image, and uses these weights to narrow the gap between the extracted attributes and the user's high-level semantic information.…”

Section: Attribute Weight Learningmentioning

confidence: 99%

Piecewise Weighting Function for Collaborative Filtering Recommendation

Zhang¹,

Li²

2022

Preprint

0

View full text Add to dashboard Cite

<p>It is not always reasonable to set a fixed weight value to an attribute (or a variable) because this cannot accurately maintain the similarity between users and leads to decrease the performance of collaborative filtering recommendation algorithms. In this paper, a piecewise weighting method is proposed with the hyper-class representation for improving the collaborative filtering recommendation. Specifically, a kernel function is first applied to map the original data into the kernel space, and to learn the weight of each attribute. And then, the hyper-class representation of the data is constructed to learn the weight of the segmented attribute value (hyper-class) for each attribute, so as to construct a piecewise weighting function. In addition, the piecewise weighting function is applied to calculate the similarity between users for a collaborative filtering recommendation. Finally, experiments are conducted to examine the collaborative filtering recommendation algorithm, and show that the proposed algorithm with piecewise weighting functions is more efficient than the compared algorithm with fixed weight values, in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Precision.</p>

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“…The use of a single video clip query falls under the scope of single query video retrieval (SQVR) while using multiple video clip queries falls under the scope of multi‐query video retrieval (MQVR). There are few content‐based multiple query retrieval studies at the image level 19‐22 . Nevertheless, video retrieval studies that have been performed fall within the scope of the SQVR 23‐28 .…”

Section: Introductionmentioning

confidence: 99%

A novel multilabel video retrieval method using multiple video queries and deep hash codes

Akbacak

¹

2022

Concurrency and Computation

0

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Most videos consist of multiple clips. Namely, videos usually have multiple spatio-temporal information. However, existing video datasets are of single video clips.Additionally, content-based video retrieval methods have been implemented based on a single query clip. However, a single query may not be sufficient for better expressing intention concerning the query. Besides, it narrows the capability to search for multiple semantics. How to deal with retrieving videos having multiple clips has not been adequately investigated. This study proposes a novel multilabel video retrieval method that utilizes multiple video clips as queries. Whatever the number of queries, it transforms a multi-query video retrieval into a single-query video retrieval. The method is independent of both the video features and distance metric types. However, we propose a deep video hashing method to achieve speed and efficiency. Experiments performed on three prepared multilabel video datasets have proven the effectiveness of the proposed method. The source codes of the study are public. K E Y W O R D Scontent-based video retrieval, multi-query optimal point, multiple video queries, objective space, video hashing INTRODUCTIONContent-based video retrieval (CBVR) is the process of retrieving videos from a video database by querying them based on visual and temporal content. The visual content is usually an object, face, color, texture, or scene at the frame level. On the other hand, the temporal content is an action type at the video level. Videos frames, each of which is an image, usually have multilabel characters. At the video level, the temporal relation between frames also expresses a piece of semantic information. 1,2 It is called action type. An activity on a football field other than playing football tends to the football semantic in terms of frame-level. However, at the video level, the action type is not football.Neglecting temporal dependencies is sometimes an obligation, but sometimes a deficiency. [3][4][5] For face or object retrieval, for example, it is not necessitated. [6][7][8][9] However, some CBVR studies fundamentally ignore temporal properties. Janwe et al. used video segmentation to get the video shots. 10 From each shot, keyframes that adequately represent the semantic meaning of each shot are extracted. Then the keyframe features are extracted and are trained in a multi-stream CNN classifier. Finally, the classification scores of each shot are fused to obtain the final class score. Similar studies are based on the shot boundary detection and keyframe extraction technique. 11,12 In such studies, video semantics are only considered on the frame level. There have been various studies considering spatio-temporal characteristics. [13][14][15][16][17] On the other hand, few studies only consider actions but ignore pixel-level features. 14,18 The most concise video scene with at least one semantic meaning at the video level is called a clip. Naturally, videos made up of clips are multilabel. The use of a single video clip qu...

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Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning

Cited by 23 publications

References 25 publications

Piecewise Weighting Function for Collaborative Filtering Recommendation

Piecewise Weighting Function for Collaborative Filtering Recommendation

Piecewise Weighting Function for Collaborative Filtering Recommendation

A novel multilabel video retrieval method using multiple video queries and deep hash codes

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