Large-scale interactive exploratory visual search

Large scale visual search has been one of the challenging issues in the era of big data. It demands techniques that are not only highly effective and efficient but also allow users conveniently express their information needs and refine their intents. In this thesis, we focus on developing an exploratory framework for large scale visual search. We also develop a number of enabling techniques in this thesis, including compact visual content representation for scalable search, near duplicate video shot detection, and action based event detection. We propose a novel scheme for extremely low bit rate visual search, which sends compressed visual words consisting of vocabulary tree histogram and descriptor orientations rather than descriptors. Compact representation of video data is achieved through identifying keyframes of a video which can also help users comprehend visual content efficiently. We propose a novel Bag-of-Importance model for static video summarization. Near duplicate detection is one of the key issues for large scale visual search, since there exist a large number nearly identical images and videos. We propose an improved near-duplicate video shot detection approach for more effective shot representation. Event detection has been one of the solutions for bridging the semantic gap in visual search. We particular focus on human action centred event detection. We propose an enhanced sparse coding scheme to model human actions. Our proposed approach is able to significantly reduce computational cost while achieving recognition accuracy highly comparable to the state-of-the-art methods. At last, we propose an integrated solution for addressing the prime challenges raised from large-scale interactive visual search. The proposed system is also one of the first attempts for exploratory visual search. It provides users more robust results to satisfy their exploring experiences

Deep Learning for Logo Detection: A Survey

When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, etc. have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and the strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection to complete this survey

A Review on Personalized Tag based Image based Search Engines

The development of social media based on Web 2.0, amounts of images and videos spring up everywhere on the Internet. This phenomenon has brought great challenges to multimedia storage, indexing and retrieval. Generally speaking, tag-based image search is more commonly used in social media than content based image retrieval and content understanding. Thanks to the low relevance and diversity performance of initial retrieval results, the ranking problem in the tag-based image retrieval has gained researchers� wide attention. We will review some of techniques proposed by different authors for image retrieval in this paper

Browse-to-search

This demonstration presents a novel interactive online shopping application based on visual search technologies. When users want to buy something on a shopping site, they usually have the requirement of looking for related information from other web sites. Therefore users need to switch between the web page being browsed and other websites that provide search results. The proposed application enables users to naturally search products of interest when they browse a web page, and make their even causal purchase intent easily satisfied. The interactive shopping experience is characterized by: 1) in session - it allows users to specify the purchase intent in the browsing session, instead of leaving the current page and navigating to other websites; 2) in context - -the browsed web page provides implicit context information which helps infer user purchase preferences; 3) in focus - users easily specify their search interest using gesture on touch devices and do not need to formulate queries in search box; 4) natural-gesture inputs and visual-based search provides users a natural shopping experience. The system is evaluated against a data set consisting of several millions commercial product images. © 2012 Authors

Location Estimation of a Photo: A Geo-signature MapReduce Workflow

Location estimation of a photo is the method to find the location where the photo was taken that is a new branch of image retrieval. Since a large number of photos are shared on the social multimedia. Some photos are without geo-tagging which can be estimated their location with the help of million geo-tagged photos from the social multimedia. Recent researches about the location estimation of a photo are available. However, most of them are neglectful to define the uniqueness of one place that is able to be totally distinguished from other places. In this paper, we design a workflow named G-sigMR (Geo-signature MapReduce) for the improvement of recognition performance. Our workflow generates the uniqueness of a location named Geo-signature which is summarized from the visual synonyms with the MapReduce structure for indexing to the large-scale dataset. In light of the validity for image retrieval, our G-sigMR was quantitatively evaluated using the standard benchmark specific for location estimation; to compare with other well-known approaches (IM2GPS, SC, CS, MSER, VSA and VCG) in term of average recognition rate. From the results, G-sigMR outperformed previous approaches.Location estimation of a photo is the method to find the location where the photo was taken that is a new branch of image retrieval. Since a large number of photos are shared on the social multimedia. Some photos are without geo-tagging which can be estimated their location with the help of million geo-tagged photos from the social multimedia. Recent researches about the location estimation of a photo are available. However, most of them are neglectful to define the uniqueness of one place that is able to be totally distinguished from other places. In this paper, we design a workflow named G-sigMR (Geo-signature MapReduce) for the improvement of recognition performance. Our workflow generates the uniqueness of a location named Geo-signature which is summarized from the visual synonyms with the MapReduce structure for indexing to the large-scale dataset. In light of the validity for image retrieval, our G-sigMR was quantitatively evaluated using the standard benchmark specific for location estimation; to compare with other well-known approaches (IM2GPS, SC, CS, MSER, VSA and VCG) in term of average recognition rate. From the results, G-sigMR outperformed previous approaches

Semantic interpretation of events in lifelogging

The topic of this thesis is lifelogging, the automatic, passive recording of a person’s daily activities and in particular, on performing a semantic analysis and enrichment of lifelogged data. Our work centers on visual lifelogged data, such as taken from wearable cameras. Such wearable cameras generate an archive of a person’s day taken from a first-person viewpoint but one of the problems with this is the sheer volume of information that can be generated. In order to make this potentially very large volume of information more manageable, our analysis of this data is based on segmenting each day’s lifelog data into discrete and non-overlapping events corresponding to activities in the wearer’s day. To manage lifelog data at an event level, we define a set of concepts using an ontology which is appropriate to the wearer, applying automatic detection of concepts to these events and then semantically enriching each of the detected lifelog events making them an index into the events. Once this enrichment is complete we can use the lifelog to support semantic search for everyday media management, as a memory aid, or as part of medical analysis on the activities of daily living (ADL), and so on. In the thesis, we address the problem of how to select the concepts to be used for indexing events and we propose a semantic, density- based algorithm to cope with concept selection issues for lifelogging. We then apply activity detection to classify everyday activities by employing the selected concepts as high-level semantic features. Finally, the activity is modeled by multi-context representations and enriched by Semantic Web technologies. The thesis includes an experimental evaluation using real data from users and shows the performance of our algorithms in capturing the semantics of everyday concepts and their efficacy in activity recognition and semantic enrichment