Research

Author: Xiang Fu, Sanjay Purushotham, Daru Xu, and C.-C. Jay Kuo

Rapid development of video sharing over the Internet creates a large number of videos every day. It is an essential task to organize or classify tons of Internet images or videos automatically online, which will be mostly helpful to the search of useful videos in the future, especially in the applications of video surveillance, image/video retrieval, etc.

One classic method to categorize videos for human is based on which makes content-based video analysis a hot topic. An object is undoubtedly the most significant component to represent the video content. Object recognition and classification plays a significant role for intelligent information processing.

The traditional tasks of object recognition and classification include two parts. One is to identify a particular object in an image from an unknown viewpoint given a few views of that object for training, which is called “multi-view specific object recognition”. Later on, researchers attempt to get the internal relation of object classes from one specific view, which develops to another task called “single-view object classification”. In this case, the object class diversity in appearance, shape, or color should be taken into consideration. These variations increase the difficulty in classification. Over the last decade, many researchers have solved the last two tasks using a concept called intra-class similarity. To further reduce the semantic gap between machine and human, the problem of “multi-view object classification” needs to be well studied.

As shown in Fig.1, there are three elements to define a view: angle, scale (distance), and height, which form the view sphere. Although the viewpoint as well as intra-class variations exist as illustrated in Fig.2, some common features can still be found for one object class by [...]

Author: Chen Chen and C.-C. Jay Kuo

An ensemble classifier, called TEAM (The Experts Assembling Machine), is proposed for the classification of large-scale indoor/outdoor images in this work. Instead of applying a single classification method to a large number of extracted image features, TEAM integrates decisions made by a set of individual classifiers, where each of them is called an expert. Although the classification performance of an expert is reasonable for small datasets, its performance degrades as the dataset size increases. TEAM offers robust and accurate classification performance for a large-scale indoor/outdoor image dataset since it allows experts to compensate each other’s weakness in face of diversified image data. We conduct experiments on an image dataset containing around 100,000 images, and show that TEAM can improve the classification accuracy of individual experts by a margin ranging from 6-20%.

We set a benchmark for Indoor/Outdoor classification algorithms by releasing a dataset with around 100,000 indoor/outdoor images. Image examples can be seen in Fig.1. We also implemented and released 7 individual experts, in order to benchmark the strength of TEAM. Related source code can be found in the links below.

Fig. 1

The proposed TEAM stands on the shoulders of state-of-the-art indoor/outdoor image classification methods. Unlike other ensemble learning algorithms (such as Adaboost and Random Forests), TEAM learned the complementariness between different algorithms at the decision level. Decisions by different methods are achieved by different low-level features and different classification methods. As a result, TEAM can perform more robustly while the data size increases. In addition, TEAM can also serve as a framework for all scene classification problems (not restricted to indoor/outdoor image classification).

For the future, there are two directions to do.

Currently, we only focus on low-level feature experts. We [...]

Author: Xiaqing Pan and C.-C. Jay Kuo

3-D Object Classification & Retrieval problem can be stated as identifying a correct class or retrieve relevant objects for a query object. In the past years, researchers developed several useful signatures to describe 3-D objects in a compact way. They focused on global description, graph description and local description. However, most of these signatures cannot handle a generic database well because of their limitations on differentiating 3-D objects in different shapes, poses and surface properties. My research is aiming to develop a sophisticated signature and a complete classification and retrieval scheme to produce high retrieval performance and classification accuracy on a generic database.

Global signatures such as [1] [2] [3] try to handle capture the global shape basis in a 3-D object but lose the details. Local signature such as [4] starts from local salient points and then builds up a statistical signature for an entire mesh but it is not robust under large shape variance. Graph signatures extract the topological information from a mesh and analyze it but only effective to limited cases. Our idea is going to conquer the limitations from the previous researches and design a natural description for 3-D objects, which highly complies with human perception.

Challenges will be conquered in the future.

Ability to differentiate and group objects with different and similar shapes
Robustness under large pose changes
Adaptiveness to variance in surface properties

[1] Osada R, Funkhouser T, Chazelle B, Dobkin D (2002) Shape distributions. ACM Trans Graph 21(4):807–832
[2] Shen Y-T, Chen D-Y, Tian X-P, Ouhyoung M (2003) 3D model search engine based on lightfield descriptors. In: Proc. eurographics 2003
[3] Kazhdan M, Funkhouser T, Rusinkiewicz S (2003) Rotation invariant spherical [...]