An integrated semantic-based approach in concept based video retrieval

Multimedia content has been growing quickly and video retrieval is regarded as one of the most famous issues in multimedia research. In order to retrieve a desirable video, users express their needs in terms of queries. Queries can be on object, motion, texture, color, audio, etc. Low-level representations of video are different from the higher level concepts which a user associates with video. Therefore, query based on semantics is more realistic and tangible for end user. Comprehending the semantics of query has opened a new insight in video retrieval and bridging the semantic gap. However, the problem is that the video needs to be manually annotated in order to support queries expressed in terms of semantic concepts. Annotating semantic concepts which appear in video shots is a challenging and time-consuming task. Moreover, it is not possible to provide annotation for every concept in the real world. In this study, an integrated semantic-based approach for similarity computation is proposed with respect to enhance the retrieval effectiveness in concept-based video retrieval. The proposed method is based on the integration of knowledge-based and corpus-based semantic word similarity measures in order to retrieve video shots for concepts whose annotations are not available for the system. The TRECVID 2005 dataset is used for evaluation purpose, and the results of applying proposed method are then compared against the individual knowledge-based and corpus-based semantic word similarity measures which were utilized in previous studies in the same domain. The superiority of integrated similarity method is shown and evaluated in terms of Mean Average Precision (MAP)

Measuring the influence of concept detection on video retrieval

There is an increasing emphasis on including semantic concept detection as part of video retrieval. This represents a modality for retrieval quite different from metadata-based and keyframe similarity-based approaches. One of the premises on which the success of this is based, is that good quality detection is available in order to guarantee retrieval quality. But how good does the feature detection actually need to be? Is it possible to achieve good retrieval quality, even with poor quality concept detection and if so then what is the 'tipping point' below which detection accuracy proves not to be beneficial? In this paper we explore this question using a collection of rushes video where we artificially vary the quality of detection of semantic features and we study the impact on the resulting retrieval. Our results show that the impact of improving or degrading performance of concept detectors is not directly reflected as retrieval performance and this raises interesting questions about how accurate concept detection really needs to be

On Semantic Similarity in Video Retrieval

Current video retrieval efforts all found their evaluation on an instance-based assumption, that only a single caption is relevant to a query video and vice versa. We demonstrate that this assumption results in performance comparisons often not indicative of models' retrieval capabilities. We propose a move to semantic similarity video retrieval, where (i) multiple videos/captions can be deemed equally relevant, and their relative ranking does not affect a method's reported performance and (ii) retrieved videos/captions are ranked by their similarity to a query. We propose several proxies to estimate semantic similarities in large-scale retrieval datasets, without additional annotations. Our analysis is performed on three commonly used video retrieval datasets (MSR-VTT, YouCook2 and EPIC-KITCHENS).Comment: Accepted in CVPR 2021. Project Page: https://mwray.github.io/SSVR

Efficient and effective similarity-based video retrieval

The retrieval of videos of interest from large video collections is a main open problem which calls for the definition of new video content characterization techniques in term of both visual descriptors and semantic annotations. In this paper, we present an efficient and effective video retrieval system which profitably exploits the functionalities offered by a semantic-based automatic video annotator using video shots similarity to suggest relevant labels for the videos to be annotated. Similarity queries based on semantic labels and/or visual features are implemented and experimentally compared on real data in order to measure the retrieval contribution of each type of video content information

Combination of semantic word similarity metrics in video retrieval

Multimedia Information Retrieval is one of the most challenging issues. Search for knowledge in the form of video is the main focus of this study. In recent years, there has been a tremendous need to query and process large amount of video data that cannot be easily described. There is a mismatch between the low-level interpretation of video frames and the way users express their information needs. This issue leads to the problem named semantic gap. To bridge semantic gap, concept-based video retrieval has been considered as a feasible alternative technique for video search. In order to retrieve a desirable video shot, a query should be defined based on users’ needs. In spite of the fact that query can be on object, motion, texture, color and so on, queries which are expressed in terms of semantic concepts are more intuitive and realistic for end users. Therefore, a concept-based video retrieval model based on the combination of the knowledge-based and corpus-based semantic word similarity measures is proposed with respect to bridging semantic gap and supporting semantic queries. In this study, Latent Semantic Analysis (LSA) which is a corpus-based semantic similarity measure is compared to previously utilized corpus-based measures. In addition, we experiment a combination of LSA with a knowledge-based semantic similarity measure in order to improve the retrieval effectiveness. For evaluation purpose, TRECVID 2005 dataset is utilized as well. As experimental results show, combination of knowledge-based and corpus-based outperforms individual one with MAP of 16.29%

Learning Semantic and Visual Similarity for Endomicroscopy Video Retrieval

Traditional Content-Based Image Retrieval (CBIR) systems only deliver visual outputs that are not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval that computes a visual signature for each video. In this study, we first leverage semantic ground-truth data to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that our visual-word-based semantic signatures enable a recall performance which is significantly higher than those of several state-of-the-art methods in CBIR. In a second step, we propose to improve retrieval relevance by learning, from a perceived similarity ground truth, an adjusted similarity distance. Our distance learning method allows to improve, with statistical significance, the correlation with the perceived similarity. Our resulting retrieval system is efficient in providing both visual and semantic information that are correlated with each other and clinically interpretable by the endoscopists

Semantics-based selection of everyday concepts in visual lifelogging

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach