Gamification is the use of game design elements and game mechanisms in non-game context to increase audience’s motivation and engagement. Not only being applied on the commercial market to attract consumers, gamification is also used in educational context by teachers to help keeping students engaged and motivated in the physical classrooms. This paper furthermore upraises the discussion of how the gamification strategies can be implemented online for a website with academic purposes to retain its members, and what of which are the members’ most concerns a designer should adapt to if time and resource are limited. Examples are given and an online survey of 73 subjects with empirical experience was conduct to reveal that among those game mechanisms proposed, the graphical feedbacks are the subjects’ most concerns, followed by the gamified theme activity and discussion board. For websites which are not gameplay purposed this paper could provide useful suggestions for the designers who try to use gamification for enhancing the audience’s participation and enthusiasm, even for educational purposes.

A fuzzy logic framework is proposed to alleviate two problems in traditional CBIR systems, including the semantic gap and the perception subjectivity. The proposed framework consists of two major parts, including (1) model construction and (2) query comparison. In the model construction part, fuzzy linguistic terms with associated fuzzy membership functions are automatically generated through an unsupervised fuzzy clustering algorithm. The linguistic terms provide a nature way of expressing user's concepts, and the membership functions characterize the mapping between image features and human visual concepts. We also define the syntax and semantics rules of a query description language to unify the query expression of textual descriptions, visual examples, and relevance feedbacks. In the query comparison part, a similarity function is inferred based on user's feedbacks to measure the similarity between the query and each image in the database. The user's preference is also captured and retained in his/her own profile to achieve personalization. Our work provides a unified and comprehensive framework for incorporation a fuzzy approach into CBIR systems. To verify our CBIR framework, we select Tamura features to describe and retrieve texture images. Experimental results show that the proposed framework is indeed effective to alleviate the semantic gap and the perception subjectivity problems.

In this study, we propose a simple and effective approach to synthesize new motions from a given sequence of continuous motion capture data. First an index function, based on posture features of each motion frame, is introduced to segment the given motion capture data into indexed motion clips. Then based on the fact that motion coherence implies index coherence, a new motion with start frame f/sub start/ and end frame fend can be synthesized by finding a smooth path connecting f/sub start/ to f/sub end/ in the multidimensional index space. Moreover, an algorithm for finding smooth paths is presented and a relevance feedback mechanism is provided to refine the results. The merit of the proposed framework is that it can generate fast prototyping of desired motions with only a small amount of preprocessing time. Experimental results are given to shown the effectiveness of the proposed framework.

In this study, we propose a fuzzy logic CBIR system for textures, named LinStar Texture (i.e., Linguistic Star for Textures). The proposed system consists of two major phases, including database creation and query comparison. In the database creation phase, six Tamura features are extracted to describe each texture image in the database. A term set on each Tamura feature is generated through a fuzzy clustering algorithm so that degrees of appearance for the feature can be interpreted as five linguistic terms. In the query comparison phase, a user can pose textual descriptions or visual examples to find the desired textures. Furthermore, the query can be expressed as a logic composition of linguistic terms or Tamura feature values. The final similarity is then computed by aggregating each individual similarity through min-max composition rules. Experimental results reveal the proposed system is indeed effective. The retrieved images are perceptually satisfactory. The retrieval time is very fast.

In this correspondence, a new approach to retrieving images from a color image database is proposed. Each image in the database is represented by a two-dimensional pseudo-hidden Markov model (2-D PHMM), which characterizes the chromatic and spatial information about the image. In addition, a flexible pictorial querying method is used, by which users can paint the rough content of the desired images in a query picture. Image matching is achieved by comparing the query picture with each 2-D PHMM in the database. Experimental results show that the proposed approach is indeed effective.

A new approach to retrieving images from a color image database is proposed in this paper. Each image in the database is represented by a pseudo two-dimensional hidden Markov model (2D PHMM), where both the chromatic and spatial information about the image could be adequately involved. In addition, a pictorial querying method is used, by which the users can paint only rough contents of the desired images if their information concerning the desired images is not crisp. The use of the flexible querying mechanism and 2D PHMMs eliminates the drawbacks of utilizing textual descriptions to retrieve images. Furthermore, effective statistical matching between the query picture and each image in the database can be achieved. The probability of the query picture generated by each 2D PHMM is just considered as the matching score between the query and the corresponding image. The images which best satisfy the query can thus be retrieved. Promising experimental results show the feasibility and effectiveness of the proposed approach.